# Privacy Research with Marginalized Groups:
What We Know, What’s Needed, and What’s Next

Shruti Sannon<EMAIL_ADDRESS>University of MichiganAnn ArborMIUSA and
Andrea Forte<EMAIL_ADDRESS>Drexel UniversityPhiladelphiaPAUSA

###### Abstract.

People who are marginalized experience disproportionate harms when their
privacy is violated. Meeting their needs is vital for developing equitable and
privacy-protective technologies. In response, research at the intersection of
privacy and marginalization has acquired newfound urgency in the HCI and
social computing community. In this literature review, we set out to
understand how researchers have investigated this area of study. What topics
have been examined, and how? What are the key findings and recommendations?
And, crucially, where do we go from here? Based on a review of papers on
privacy and marginalization published between 2010–2020 across HCI,
Communication, and Privacy-focused venues, we make three main contributions:
(1) we identify key themes in existing work and introduce the Privacy
Responses and Costs framework to describe the tensions around protecting
privacy in marginalized contexts, (2) we identify understudied research topics
(e.g., race) and other avenues for future work, and (3) we characterize trends
in research practices, including the under-reporting of important
methodological choices, and provide suggestions for establishing shared best
practices for this growing research area.

privacy, marginalization, discrimination, literature review

††copyright: acmlicensed††journal: PACMHCI††journalyear: 2022††journalvolume:
††journalnumber: CSCW††ccs: Security and privacy Human and societal aspects of
security and privacy††ccs: Human-centered computing

##  1\. Introduction

People who face marginalization in society—whose needs and experiences are
overlooked and who have limited resources and power due to some facet of their
identity (Cook, 2008)—can have unique privacy-related needs and behaviors that
must be recognized by researchers and designers of technology. Marginalized
groups can experience disproportionate harms when their privacy is violated.
Consider the case of a person living with HIV, who may risk social stigma,
discrimination, or the dissolution of relationships should their HIV status
become publicly known without their consent (Greene et al., 2003), as compared
to the impact of a health information leak for a person who does not have to
navigate a marginalized and often stigmatized identity. In response to these
elevated privacy threats, marginalized individuals have to navigate
complicated decisions around identity management and disclosure, which extend
to their technology use (Sannon et al., 2019). At the same time,
marginalization can also constrain privacy-protective behaviors; for example,
people in economically disadvantaged communities often have limited access to
privacy literacy resources (Li et al., 2018). Understanding the new dimensions
and consequences that privacy issues take on in marginalized contexts is vital
to designing more equitable technologies that respect the privacy of all users
rather than a select few.

Given the need to represent marginalized voices in the design of technologies,
and the potential privacy harms that stem from excluding them, there has been
growing interest in this research area  (Walker et al., 2019). Wang suggests
that we are currently seeing a third wave in privacy research that centers
what he terms as “inclusive privacy” that encompasses “different human
abilities, characteristics, needs, identities, and values,” as compared to
prior waves that focused on the technical and usability aspects of privacy
(Wang, 2017, 2018). Concurrently, privacy researchers in this space have begun
to convene via panels (Das et al., 2020) and workshops, such as the 2017 CSCW
workshop on privacy for vulnerable populations (McDonald et al., 2020), and
the 2015-2022 SOUPS workshops on inclusive privacy and security (WIPS),
pointing to a clear interest in this research area.

What remains unclear is how the work being conducted by researchers in this
area coheres together, the bounds of communal knowledge in this space, and
what gaps still need to be filled. Literature reviews can be useful to assess
and guide new, emerging areas of research (Dillahunt et al., 2017). Literature
reviews have been used to this end in computing research, including at CSCW
and CHI (e.g., (Dillahunt et al., 2017; DiSalvo et al., 2010)). Given the
rapidly growing interest and research in this space, we see this as a key
moment in time for a literature review that could help spur new research in
this area while also providing an understanding of our cumulative knowledge
and practices thus far.

To this end, we conducted a literature review of privacy research on
marginalized populations,  using a broad and open-ended definition of
“marginalized adults” as adults facing any form of social exclusion or
discrimination due to some facet of their identity (Hall et al., 1994; Cook,
2008), such as along the lines of race, sexual identity, gender identity,
socioeconomic status, and immigration status, among other factors. We examined
papers published between 2010–2020 across a broad range of venues representing
three inter-related disciplines: HCI and Social Computing (e.g., Proceedings
of CSCW, CHI, and Ubicomp), Communication (e.g., Journal of Communication and
New Media and Society), and Privacy-focused venues (e.g., Proceedings of the
Privacy Enhancing Technologies Symposium).

After manually reviewing 2,823 privacy-related papers that were published
across these disciplines between 2010-2020, we identified 88 papers that
focused on the intersection of privacy and marginalization. Based on an
analysis of these 88 papers, we offer the following contributions:

  * •

_Key Research Areas and Findings._ We describe the major contexts of
marginalization studied and the kinds of findings and recommendations that
papers in our dataset put forward.  In doing so, we introduce the Privacy
Responses and Costs framework, which outlines 10 privacy responses people have
to privacy threats, and the costs and consequences of these responses faced by
marginalized groups.

  * •

_Descriptive Norms for Research Practices._ We characterize the literature in
terms of the practices that researchers report using to study privacy and
marginalization.

  * •

_Gaps and Implications._ Having surveyed the literature, we identify
understudied areas and questions (e.g., race), lack of diversity in methods,
and under-reporting of important methodological choices, and we make concrete
recommendations for establishing shared best practices for this growing
research area, such as designing protocols that minimize harm.

##  2\. Related Work

Given our focus on marginalization, we begin by explaining what we mean when
we use this term. Then, we discuss the use of literature reviews in computing
research broadly and privacy research both within and outside of HCI and
social computing (for brevity, we refer to this field as just “HCI” in what
follows). We end by discussing the focus of the current review.

###  2.1. What Do We Mean by Marginalization?

Although the terms “marginalization” or “marginalized” are sometimes used in
studies of technology use and privacy concerns, the term is commonly left
undefined in HCI and social computing work. In our findings, we used a
grounded approach to identify the ways that researchers engage and discuss
marginalization in the privacy literature; however, to begin the task of
identifying research about marginalization, we needed to define precisely what
we mean when we use the term.

Marginalization is a complicated concept with varying definitions and
dimensions. We drew from several sources to construct our frame for
“marginalized contexts.” Marginalized populations are defined as “persons who
are peripheralized based on their identities, associations, experiences and
environments” (Hall et al., 1994, p. 25). As a result, these groups are
“excluded from mainstream social, economic, cultural, or political life”
(Cook, 2008). People can be marginalized based on several factors, including
race, disability, gender identity, sexual orientation, socioeconomic status,
and immigration status.

The impact of marginalization can be seen not only in the ways groups are
peripheralized, but also in social responses to this exclusion.
Marginalization has been acknowledged through legal protections around the
world through laws prohibiting discrimination in housing, employment, and
other areas of life (e.g., the Fair Housing Act in the U.S. (of Justice,
2021),  the Racial Equality directive in the European Union (Council of
European Union, 2014), and the Rights of Persons with Disabilities Act in
India (Government of India, 2016)).

In this review, we focus on research about marginalized adults, many of whom
face discrimination that these legal responses were set up to address.
Although we were undoubtedly influenced by these readings and familiar a
priori categories of marginalization commonly represented as legally protected
classes, we conducted an open-ended analysis: if a paper described a group as
being stigmatized or marginalized, or referred to privacy and marginalization
more broadly, we opted to include it. As a result, our review includes a wide
swath of marginalized identity characteristics, such as disability, sexual
orientation, socioeconomic status, immigration status, and race, as well as
experiences like sex work and human trafficking.

Although the studies we review in this paper are included because they study
populations that have the characteristic of being “marginalized,”
marginalization is not simply a feature of a population or individual, but a
dynamic social process of exclusion that marshals the power of social norms,
institutions, and interpersonal dynamics to render some people as privileged
and others as inconsequential. As privacy researchers, we use this process
orientation to understand privacy risks and violations as features of
marginalization that change over time and in different contexts. As social
computing scholars, we pay special attention to the role that technology
design plays in initiating, continuing, accelerating, stalling, or obstructing
processes of marginalization.

###  2.2. Why do a Literature Review? Reviews in HCI and Social Computing

Literature reviews are not uncommon in HCI and social computing venues, and
can provide insight into both the knowledge built by a research community as
well as the practices used to construct this knowledge. First, literature
reviews are a useful way to identify research trends and unearth new
directions for research communities, and have been used to this effect to
further research on the sharing economy (Dillahunt et al., 2017), HCI for
development (Dell and Kumar, 2016), and sustainable HCI (DiSalvo et al.,
2010), among other areas.

Second, literature reviews can also help develop clarity around methods and
direct best practices for a research community. To this effect, literature
reviews have provided insight and instruction around the use of reliability
measures (McDonald et al., 2019), the reporting of compensation (Pater et al.,
2021), the standards for sample size (Caine, 2016), and anonymization
practices (Abbott et al., 2019) in social computing research.

Our goal is for this review to serve both of these purposes: (1) to understand
the existing knowledge generated by researchers on privacy issues in
marginalized contexts while pinpointing new areas of exploration, and (2) to
understand the methods through which this knowledge generation has occurred
with an eye towards guiding future research practices.

###  2.3. Reviews of Privacy Research

Literature reviews are also not new to privacy research, both within and
outside the HCI research community. Some of these reviews have explored
conceptual or theoretical approaches in privacy research, such as how design
relates to privacy in HCI research (Wong and Mulligan, 2019), how researchers
have theorized about the privacy paradox (Barth and De Jong, 2017), and how
privacy has been conceptualized in HCI (Barkhuus, 2012).

Alternatively, several privacy-related reviews have focused on the privacy
considerations of specific technologies or contexts, such as eye-tracking
(Katsini et al., 2020), cryptocurrencies (Herskind et al., 2020), Internet of
Things (IoT) infrastructures (Alshohoumi et al., 2019), big data (Nelson and
Olovsson, 2016), or electronic health record systems (Mahfuth et al., 2016;
Rezaeibagha et al., 2015). These reviews have put forth several implications
for privacy research, including identifying privacy threats that still need to
be addressed (Herskind et al., 2020), and identifying a dearth of research on
privacy protection in relation to the specific technologies under study
(Katsini et al., 2020; Alshohoumi et al., 2019).

###  2.4. Our Focus

It is clear that literature reviews can be useful in evaluating nascent
computing fields and subfields and unearthing future directions for research
communities. We searched the ACM and other databases to determine whether a
review of privacy research on marginalized contexts had been conducted, and to
our knowledge, no such review exists. Given both the importance of and growing
interest in this subfield of privacy, we aim to provide a review that will tie
existing work together, highlight key similarities and differences in the
subfield’s knowledge and research practices, and provide recommendations for
future research.

Having established the need for a review of research on privacy and
marginalization, we drew on existing literature review practices to
appropriately scope our inquiry. First, many literature reviews published in
HCI and social computing venues (e.g., (Abbott et al., 2019; McDonald et al.,
2019)) focus on research published by the ACM. Since our primary audience is
the HCI and CSCW community, we also chose to focus on HCI research published
by the ACM. However, we also wanted to acknowledge the blurry nature of
interdisciplinary boundaries. This led us to review a sample of publications
from adjacent disciplines as well. Toward this end, we also examined papers
published in a selection of high-impact Communication journals like New Media
and Society and Privacy-focused venues like IEEE Privacy & Security.

Second, many social computing literature reviews are scoped by time period. We
found considerable variation among the publication periods examined by
reviews, ranging from three years (McDonald et al., 2019) to ten years
(Dillahunt et al., 2017). We scoped our review to papers published from 2010
through 2020 to capture a broad range of papers and measure activity in this
research area over time.

As discussed in Section 2.2, reviews can synthesize knowledge in a domain,
identify areas ripe for exploration, and develop best practices for a research
community. Accordingly, our review is guided by a few overarching questions to
achieve these goals. The first set involves the breadth of research in this
area and key findings:

> What research has been done on privacy in marginalized contexts? What can we
> learn from this cumulative knowledge about how marginalization relates to
> privacy, and what are the opportunities for future research directions in
> this space?

The second set of questions involves the practice of doing research on this
topic:

> How has this research been done? What can we learn in terms of best
> practices for conducting privacy research on marginalized contexts?

##  3\. Method

Our method for conducting the review was made up of four main stages: 1)
collecting a corpus of privacy-related papers in multiple fields and venues,
2) filtering this dataset to only include papers that focus on both privacy
and marginalization, 3) conducting a quantitative analysis to identify
descriptive trends (e.g., common publication venues), and 4) conducting a
qualitative thematic analysis to understand themes in the papers’ approaches,
findings, and recommendations.

In what follows, we explain the steps we took at each stage of this process.
We also present an overview of our method using the Preferred Reporting Items
for Systematic Reviews and Meta-Analysis (PRISMA) schematic of study flow
(Moher et al., 2009) in Figure 1.

###  3.1. Data Collection

To reflect the diversity of research on privacy, we sampled papers from three
distinct but interdisciplinary and overlapping fields: HCI, Communication, and
Privacy-focused venues.  Our goal was to review a broad set of papers that
represent research in these fields, rather than attempting to collect the
complete set of papers published on privacy and marginalization.

Data collection took place in 2021. Per guidelines for effective literature
reviews (Khan et al., 2003), we developed selection criteria to identify
relevant papers for inclusion in the review: (1) full research articles (i.e.,
not posters, abstracts, panels, and so forth), (2) published between 2010 and
2020, (3) with a focus on both privacy and marginalization. In this section,
we describe our process for identifying search terms for the review, and then
detail our data collection procedures for each of the three fields.

####  3.1.1. Identifying Search Terms

We began by exploring search terms for identifying relevant papers. Since our
aim was to identify papers that were both about privacy and marginalization,
we considered using search terms for both of these foci. However, we quickly
discovered that many relevant research papers on marginalized contexts do not
use common keywords (such as “marginalization” or “vulnerable”) and would thus
be excluded from our dataset. While we considered developing a list of
marginalized contexts to use as a priori search terms (e.g., “disability”), we
decided that this would place artificial limits on the breadth of our data.

Thus, we decided to take a more expansive (albeit labor-intensive) approach:
to collect all privacy-related papers published in our selected venues to
arrive at a dataset of privacy research between 2010–2020, and then manually
sort through these papers using our selection criteria to identify papers that
also focused on marginalized contexts. The venues we selected to gain a broad
overview of HCI, Communication, and Privacy venues are listed in Table 1. To
identify privacy-related papers, we searched for the term “privacy” in papers’
titles, abstracts, and/or keywords. We considered using a more extensive set
of keywords related to privacy (e.g., surveillance, tracking, disclosure), but
found that these terms were often overly broad or narrow, and thus introduced
significant noise in the dataset (e.g., “tracking” brought up numerous results
about eye sensor tracking that were not related to privacy). Since we were
also searching fields outside of HCI where these terms could take on
conflicting dimensions, we decided to use privacy as the sole keyword.

Venues | Journals and Proceedings
---|---
HCI | CSCW, CHI, DIS, MobileHCI, PACM CSCW, PACM GROUP, PACM IMWUT
Communication | CR, JOC, IJOC, JCMC, NM&S, SM+S
Privacy | SOUPS, PoPETS, IEEE S&P, USENIX Security

Table 1. Venues represented in the dataset comprising 8 conference proceedings
and 9 journals from 2010–2020. Note that CSCW was a proceedings through 2016
and a journal thereafter.

[Table]A table that lists the 8 conferences and 9 journals in the dataset; the
list is also provided in the text of the paper.

####  3.1.2. HCI

We searched the ACM Digital Library (ACM-DL) for papers with the term
“privacy” in either the title, abstract, and/or keywords. We used the ACM-DL
search options to restrict the results to (1) papers published between
2010–2020, and (2) papers classified as “Research article” (thus excluding
posters, workshops, abstracts, and panels).

We then selected the “sponsored by SIGCHI” option to identify papers published
in the proceedings of HCI conferences (e.g., CSCW, CHI, DIS, etc.). This
search produced 623 papers. We also collected search results for the following
journals: the Proceedings of the ACM on Human-computer Interaction (PACM) (62
papers), Proceedings of the ACM on Interactive, Mobile, Wearable and
Ubiquitous Technologies (IMWUT) (75 papers), and Transactions of Computer-
Human Interaction (TOCHI) (12 papers).

We downloaded a dataset composed of 772 HCI papers based on the above
procedure. We cleaned this dataset by removing 14 duplicates and 75 non-
research articles (e.g., posters and doctoral consortium abstracts that were
incorrectly tagged in the ACM-DL). Our HCI dataset after data cleaning
contained 683 privacy-related papers.

####  3.1.3. Communication

There is no central database for Communication papers, and literature reviews
on communication and technology typically examine a predetermined selection of
journals (e.g., (Evans et al., 2017)). Following this approach, we selected a
sample of top journals in Communication that publish papers on technology:
Journal of Communication (JOC), Journal of Computer-Mediated Communication
(JCMC), Communication Research (CR), International Journal of Communication
(IJOC), New Media and Society (NMS), and Social Media + Society (SM+S).

We searched for “privacy” on each individual journal’s website, filtering for
full research articles published during 2010–2020. This resulted in a total of
978 papers (JOC = 58, JCMC = 155, CR = 63, IJOC = 79, NMS = 380, and SM+S =
243). However, some journals’ search interfaces did not allow us to exclude
full-text searches (unlike our ACM search), and thus many papers were
irrelevant to privacy as a research topic (e.g., papers that mentioned privacy
once in the Methods section when describing a consent process). Thus, to stay
consistent with our ACM search, we manually filtered the Communication dataset
to only include papers that contained privacy in their titles, abstracts,
and/or keywords (when available). After this filtering process, the
Communication dataset consisted of 208 privacy-related papers (JOC = 8, JCMC =
13, CR = 4, IJOC = 79, NMS = 58, SM+S = 46).

####  3.1.4. Privacy-focused venues

We sampled four Privacy venues: the USENIX Symposium on Usable Privacy and
Security (SOUPS), IEEE Security and Privacy (S&P), Proceedings of the Privacy
Enhancing Technologies Symposium (PoPETS), and USENIX Security. Since these
venues are focused on privacy, we did not have to run a search to identify
privacy-related papers. Instead, we included the entire corpus of 2010–2020
papers published at SOUPS, IEEE S&P and USENIX Security in our dataset; PoPETS
began its proceedings in 2015, and we included all PoPETS papers from
2015-2020 in the dataset. The dataset from these venues consisted of 1,932
papers (SOUPS = 232, PoPETS = 306, USENIX Security = 813, IEEE S&P = 581).

Figure 1. The Preferred Reporting Items for Systematic Reviews and Meta-
Analysis (PRISMA) schematic of study flow, illustrating the data collection
and filtering process of publications in selected venues published during
2010–2020

[A visual depiction of the Preferred Reporting Items for Systematic Reviews
and Meta-Analysis (PRISMA) schematic of study flow. All of the data presented
in this graph is also given in the text, describing how we collected and
filtered the data at each stage.]

###  3.2. Data Filtering

Once we had a corpus of 2,823 privacy-related papers from HCI, Communication,
and Privacy-focused venues, we manually coded this dataset to identify papers
that focused both on privacy and marginalization. We coded each paper for
inclusion/exclusion according to the following codes: (1) relevant (the paper
is about privacy in the context of marginalization), (2) not relevant (the
paper does not cover both privacy and marginalization), and (3) unclear. We
coded each paper based on its title and abstract, only referring to the full
text of the paper if the coding decision was unclear.

We operationalized marginalization through an iterative process of reading and
discussion—in addition to reading in the areas of HCI, Communication, and
Privacy, we investigated the history of the term and read widely in journals
from other fields, books, and websites of governmental agencies and NGOs.
Definitions of marginalization in these readings were not homogeneous and
sparked lively discussions in our research group about inclusion criteria. In
cases where a group’s marginalized status was unclear, we chose to only
include papers that used language to plainly situate such populations as
marginalized.

To ensure a clear and consistent interpretation of the concepts “privacy” and
“marginalization”, we used an iterative process of discussion and testing. Two
researchers independently coded a subset of 10 papers each for relevance and
discussed their initial impressions. Then, they coded 25 more papers and met
to discuss and reconcile disagreements; at this point, their inter-rater
reliability was good (Cohen’s $\kappa$ = 0.74). Following this discussion,
they coded 25 additional papers; inter-rater reliability for this round of
coding was strong (Cohen’s $\kappa$ = 0.83). Having established close
agreement over the coding scheme, one coder then independently coded the
remaining papers in the dataset for relevance. Out of an abundance of caution,
61 papers were coded as “unclear” and flagged for further examination; through
discussion, 13 of these papers were re-coded as relevant for inclusion in the
final dataset.

Ultimately, 88 of the 2,823 privacy-related papers were coded as relevant for
inclusion in the final dataset (HCI = 55, Communication = 18, Privacy = 15).
All 88 papers were downloaded from their respective repositories for analysis.
A list of the 88 papers is available as a supplementary file to this paper on
the ACM digital library.

###  3.3. Data Analysis

During analysis, both authors read papers in the dataset; between us, we read
all 88 papers. We conducted two main analyses: a quantitative analysis to
identify descriptive trends across papers, and a qualitative analysis to
identify themes in papers’ findings and practices, as detailed below.

####  3.3.1. Quantitative Analysis

We extracted descriptive data about each paper and compiled these into a
spreadsheet. These data included each paper’s publication year, publication
venue, methods, marginalized population/context, and technological focus, if
any. We also noted whether each paper discussed ethics, compensation, and
positionality; we extracted the content of these statements—when present—into
the spreadsheet. We used these data to calculate descriptive statistics about
the dataset (e.g., how many papers were published at CSCW, how many focused on
LGBTQ+ individuals, how many provided information about compensating
participants).

####  3.3.2. Qualitative Analysis

As we read through each paper, we created open codes to identify concepts and
themes that cut across the dataset. We focused on study rationales, findings,
implications, and recommendations of each paper, as well as how authors
discussed the relationship between technology, privacy, and marginalization.
We met regularly to discuss patterns across papers. For example, we found that
several papers highlighted key privacy-related tensions in relation to
marginalization (such as the need for social support versus the need for
privacy); through discussion, we categorized these as four key tensions that
we present in Section 7.2.

The primary goal of the qualitative analysis was to inductively generate
concepts and themes, not to consistently identify examples of pre-defined
concepts; as such, we did not calculate inter-rater reliability but instead
used ongoing, iterative discussion to achieve consensus, aligned with best
practices described in McDonald et al. (2019). This is in line with several
other reviews that focus on surfacing themes in research areas (e.g., (DiSalvo
et al., 2010; Wong and Mulligan, 2019)).

##  4\. An Overview of the Dataset

We begin by providing a snapshot of the work done at the intersection of
privacy and marginalization. We describe when and where papers in our dataset
were published, and then identify the foci of these papers, both in terms of
the types of marginalization contexts and the types of technologies they focus
on. To describe our findings, we use the following notation for each main type
of venue: HCI (SIGCHI venues), C (Communication), and P (Privacy-focused
venues).

###  4.1. Publication Venues and Years

Our final dataset represented 88 papers from 17 journals or conference
proceedings. The majority of these papers were in HCI (55, 63%), followed by
Communication (18, 20%) and Privacy-focused venues (15, 17%). Figure 2
illustrates the number of papers published between 2010–2020 by venue.

Figure 2. Publication counts from 2010 to 2020 in HCI, Communication, Privacy-
focused venues, as well as the total number of papers across venues over time

[A line graph]A line graph showing a steep increase in papers from 2017
onwards

Within HCI, the majority of the 55 papers were published at CHI (24, 44%) and
CSCW (including both the conference proceedings and the PACM journal, 24,
44%), suggesting that CHI and CSCW are the key venues that HCI researchers
target for work on privacy and marginalization and that these two communities
have an orientation that makes them receptive to the work. A few were
published at other HCI venues: DIS (4, 7%), PACM IMWUT, GROUP, and MobileHCI
(1 paper each).

In Communication, a little over half of the 18 papers were from the
International Journal of Communication (10, 56%), followed by New Media and
Society (4, 22%) and Social Media + Society (2, 11%). Journal of Communication
and Communication Research included one paper each.

In Privacy-focused venues, most of the 15 papers were in SOUPS (6, 40%) and
USENIX Security (5, 33%), followed by PoPETS and IEEE S&P (2, 13.33% each).

Overall, the vast majority (71, 78%) of the 88 papers were published between
2017 and 2020, bolstering our view that this is a growing area of interest
among researchers across HCI/Social Computing, Communication, and Privacy.

###  4.2. Types of Marginalization Contexts

We found that papers could be broadly categorized as focusing on the following
types of marginalization contexts: (1) individuals and identities; (2)
physical spaces and communities; (3) online spaces, tools, and communities; or
(4) marginalization in general. We discuss each of these types below. Figure 3
presents a breakdown of paper distributions by context and publishing venue.

Figure 3. Papers by publication venue and marginalization context. The width
of each bar represents the percentage of papers in each marginalization
context across all publication venues. The height of each color bar represents
the percentage of papers in each publishing venue. Graph includes contexts
represented by at least 2 papers.

[A mosaic graph]A mosaic graph showing that the majority of papers across
contexts in the dataset are in HCI; Disability and LGBTQ+ issues make up half
of all contexts

####  4.2.1. Individuals and Identities

Most papers (72, 82%) focused on specific marginalized identities (HCI = 46, C
= 13, P = 13). Disability was the most popular topic (28, 32%) in the dataset,
comprising more than a third of all HCI papers (21) and approximately half of
papers at Privacy-focused venues (7). There were no Communication papers in
this area. The most common disability topics included dementia and age-related
cognitive impairments (HCI = 8, P = 4), visual impairments (HCI = 2, P = 4),
and HIV (HCI = 5). In contrast, only three papers explored mental health
conditions.

The second most common focus was individuals who identify as LGBTQ+ (14, 16%);
within this category, most (8) papers focused on trans individuals. Several
papers examined intimate partner abuse (9, 10%), low-income individuals (6,
7%), and the risks faced by women in patriarchal South Asian contexts (5, 6%).
There were also several papers that focused on contexts that often (but not
exclusively) involve women, including sex work (1), sexual assault (1) and
human trafficking (1), as well as women transitioning from incarceration (1).

Only two papers examined race and ethnicity as their primary focus; both in
Communication. A few papers studied groups that were also often racial or
ethnic minorities: undocumented immigrants (2), politically marginalized
minorities (1), and refugees (1).

####  4.2.2. Physical Spaces and Communities

Rather than focusing on individuals, some studies (10, 11%) focused on
marginalization at the level of communities (HCI = 6, C = 3, P = 1). Most of
these papers (8) examined communities that are economically disadvantaged,
while two examined other factors of marginalization: crime, income, and racial
segregation across neighborhoods (Israni et al., 2017) and communities that
are “geographically and culturally marginal” (Jack et al., 2019).

####  4.2.3. Online Spaces, Tools, and Communities

Two HCI papers in the dataset focused on tools that marginalized groups use to
protect their privacy online or communities in which marginalized people
participate: Tor, open-source software that enables anonymous communication
(Forte et al., 2017), and online fandom communities (Dym and Fiesler, 2020).

####  4.2.4. Marginalization in General

Four papers did not focus on a particular context of marginalization: One non-
empirical paper theorized about how to integrate vulnerability and
marginalization in privacy theories and research (McDonald and Forte, 2020).
Two explored how sensitive data about people can be identified through their
digital traces (Maris et al., 2020; Cabañas et al., 2018), and how these
privacy violations can be particularly concerning for populations with
stigmatized or marginalized attributes. One paper examined how the digital
divide impacts marginalized groups more broadly (Gangadharan, 2017).

###  4.3. Types of Technologies

In addition to identifying the marginalization context of papers, we
identified the types of technologies they focused on, if any. Approximately
one third of papers (31, 35%) did not focus on a specific technology but,
rather, explored technology use more broadly. This approach appeared in all
three fields (HCI = 20, C = 5, P = 6).

In terms of studies that focused on specific technologies, the most common
focus was social media (23, 26%), particularly in HCI (HCI = 18, C = 3, P =
2); these included papers that examined a broad range of social media
platforms or individual platforms, as well as different types of social media
(e.g., online forums, dating apps, social networking sites, etc.).

Mobile phones were the second most common technology (8, 9%), followed by
location-tracking technologies (5, 6%); the rest of the studies focused on
many different types of technologies, including assistive technologies,
automatic gender recognition algorithms, smart homes, and national
identification systems. In a departure from the rest of the dataset, two
Communication papers did not focus on technology or technology use at all,
instead theorizing about the relationship between marginalization and privacy.

##  5\. Why Study Privacy and Marginalization?

To study a phenomenon, it must be problematized in such a way that its
importance is clear to the members of a research community. How researchers
frame marginalization as a compelling area for privacy research not only sheds
light on the research topic, but also on the scholarly community. In this
section, we explore the rationales motivating work in this space.

###  5.1. Disproportionate risks and challenges

In many cases, studies problematized how marginalized groups may need to use
technology—particularly in service of some identity-related goal—and
simultaneously face elevated privacy risks from doing so. For example,
survivors of intimate partner violence who shared custody of a child with an
abusive ex-partner found that while electronic communication enabled remote
contact between the child and ex-partner, it also opened up the possibility
for intrusive access, such as through a live video feed of their home (Freed
et al., 2017).

Several studies were motivated by the potential ramifications of privacy
violations and their disproportionate impact on marginalized groups. Maris et
al. observe that leaked URL data can disclose sexual interests, and note that
“Those most likely to be impacted by online sexual privacy violations are
traditionally marginalized and vulnerable communities, especially women,
people of color, LGBTQ and other marginalized gender/sexuality communities”
(Maris et al., 2020). Data leaks about sexual preferences that are aligned
with normative standards may prove embarrassing, but are not likely to lead to
harassment or discrimination. However, such data leaked about sexual interests
that are perceived as non-normative or stigmatized may lead to substantial
harm.

While marginalized groups use a range of strategies to protect themselves from
privacy risks, to resist technology, and to assert their agency (Hamby et al.,
2018), they can also face disproportionate challenges in protecting their
privacy due to disparities in digital literacy, skills, technological access
as well as linguistic and cultural barriers (e.g., (Srinivasan et al., 2018;
Ahmed et al., 2015)).

###  5.2. Technological and societal exclusion

Researchers also cited technological and societal exclusion as reasons to
study marginalized groups. Marginalized groups’ access to resources and
opportunities are often bounded by their access to (and willingness to use)
technologies that can violate their privacy. Technologies can potentially
exacerbate social exclusion because of the privacy risks they pose, and in
this way, they can reify or accelerate the process of marginalization.

Some papers discussed limitations in various types of technologies, including
phones and facial recognition software, with an emphasis on the fact that
technology is often designed without consideration for the needs of
marginalized groups. Ahmed et al. explain that for disabled persons, “there
are also significant differences [in privacy concerns], due in part to their
disability but also because of systems that were not designed with them in
mind” (Ahmed et al., 2015).

Exclusion from the technology design process can create a host of privacy-
related challenges for marginalized groups that warrant examination. For
example, Dosono et al. uncover several barriers faced by people with visual
impairments in using authentication to access websites (Dosono et al., 2015).

Other papers called out structural challenges that marginalized groups face in
society that complicate experiences in realms like education and employment
that were not always tied to technology use; for example, in the case of
children of undocumented immigrants to the United States, disclosures about
immigrant status may be necessary for youth seeking support both online and
offline, but carry the oppressive threat of deportation (Kam et al., 2020).

##  6\. How Do Researchers Study Privacy and Marginalization?

The majority of papers (97%) were empirical research (HCI = 54, C = 16, P =
15). Qualitative methods were most common across the board (HCI = 49, C = 12,
P = 10), making up 81% of all papers. Here, we provide a field-specific
breakdown of methods used in this space.

In HCI, several studies used multiple methods (e.g., interviews as well as
focus groups or a survey); here, we present counts for each method used (i.e.,
some papers were counted more than once). Semi-structured interviews were the
most used qualitative research method (45), followed by focus groups (9),
participant observation (6); some papers also drew on textual content analyses
(3), participatory action research (1), and ethnographic action research (1).
Seven HCI studies used quantitative methods, including a survey (1),
experiment (1), computational techniques (2), system evaluation (2), and
quantified measures to supplement an interview study (1). In addition, seven
HCI papers involved the design, development, and/or evaluation of prototypes
or systems, such as a sound awareness system for deaf people (Jain et al.,
2020), a monitoring system for dementia care (Wan et al., 2014), and a phone-
based broadcast system to reach urban sex workers (Sambasivan et al., 2011).

Communication studies in this space were primarily qualitative, using
interviews (10), observational fieldwork (2), ethnographic methods (2), and/or
focus groups (1). The four remaining empirical Communication studies were
quantitative, comprising of 3 surveys and a quantitative content analysis.

Following the trend in HCI and Communication, all of the qualitative studies
published in Privacy-focused venues drew on interviews (9). Computational
methods were a little more common in Privacy-focused venues (4), such as
natural language processing.

Three papers in the dataset were theoretical pieces that were not based on
empirical research (HCI = 1, C = 2) (e.g., (Reichel, 2017)).

In what follows, we discuss various aspects of researchers’ methodological
approaches and research practices, in terms of the types of lenses and
frameworks they drew on, how they engage with marginalized groups, the ethical
considerations of their research, decisions around compensation, and issues of
positionality.

###  6.1. What lenses and frameworks do they draw on?

A minority of papers explicitly employed a critical lens or framework in their
research approach. Critical lenses are those that consciously reflect on how
social norms and cultures create systems of oppression. In HCI, six papers
used some form of critical lens, such as a feminist, queer-Marxist, or
intersectional approach (e.g., (Clarke et al., 2013; Sambasivan et al.,
2019)). In Communication, one paper provided a critical review of how privacy
discourse has been historically used to further disenfranchise marginalized
individuals along class and racial lines (Reichel, 2017). No studies in
Privacy-focused venues employed these approaches.

###  6.2. How do they engage with marginalized groups?

There is a growing call to engage marginalized groups directly in research
involving them, and to empower them by providing opportunities to co-develop
and shape research activities, findings, and recommendations. However,
building rapport, establishing equitable collaborations, and navigating
ethical issues like establishing appropriate consent processes and providing
appropriate compensation for research participants means that engagement can
be a complex issue for researchers.

We examined the empirical studies in our dataset to identify how many engaged
with marginalized groups as study participants. We found that participation or
direct input from marginalized groups was fairly common across fields  (HCI =
44, 80%; C = 15, 83%; P = 11, 73%).

In many studies, participants from marginalized groups took part in
interviews, surveys, and co-design sessions, among other research activities.
In some cases, researchers also interviewed other stakeholders, such as
familial caregivers and professional care workers. In three studies, the
researchers explicitly discussed difficulties in recruiting their population
of interest due to cognitive impairments that compromised both the consent
process and the nature of responses; in these cases, these difficulties were
included in the researchers’ rationale to interview other stakeholders instead
of the marginalized group themselves.

###  6.3. How do researchers discuss the ethical considerations of this work?

Questions of research ethics have become increasingly complicated in HCI
research given the wide range of methods at researchers’ disposal (Bruckman,
2014). At the same time, research ethics are a crucial consideration in
studies involving marginalized groups. Research on sensitive topics can also
engender challenges around “situational ethics”—ethical considerations that
emerge in the process of conducting research that depart from formal
procedural ethics requirements (Munteanu et al., 2015). Munteanu et al.
provide examples of situational ethics, such as discovering that some
participants are not comfortable with sharing in focus groups, and altering
the method to one-on-one interviews instead to respect participants’ needs
(Munteanu et al., 2015). In this section, we explore reporting practices about
ethical considerations.

| HCI | Communication | Privacy | Total
---|---|---|---|---
Includes ethics discussion | 30 | 3 | 10 | 43
No ethics discussion | 25 | 15 | 5 | 45

Table 2. Papers that discuss ethical considerations

[Table]A table that shows that about half of the papers in the dataset include
ethical considerations; all numbers are also provided in the text of the
paper.

As shown in Table 2, papers varied widely in whether and how they discussed
ethical considerations. Almost half (49%) discussed research ethics in some
form—either in a dedicated section or integrated within a broader Methods
section. However, this choice varied sharply by field, with most computing
papers including such discussions (HCI = 30, 55%, P= 10, 67%) in contrast to
Communication (C = 3, 17%).

Research ethics discussions ranged from cursory to extensively detailed.
Researchers most commonly reported anonymizing data (e.g., by removing
participant identifiers) and obtaining consent from participants; papers that
had short ethics discussions invariably only reported these procedures. Some
papers reported more extensively on planned or emergent ethics-related
procedures. Although some procedures were uncommon, we discuss some key themes
around privacy, trust, and support to illustrate the breadth of approaches to
ethics in this research space.

####  6.3.1. Protecting Privacy

Some papers went beyond anonymizing participant identifiers to protect
participant privacy, such as by paraphrasing quotes taken from online data
sources to reduce their searchability (Johnson et al., 2020) or not recording
audio during interviews (Sambasivan et al., 2011). One study took the uncommon
approach of not asking for demographic information from participants, instead
choosing to use existing demographic data from participants’ neighborhoods as
a proxy (Vitak et al., 2018).

####  6.3.2. Building Trust

Some studies involved developing trust with communities and/or community
allies and soliciting their guidance during the research process. Some
researchers reported developing relationships with community members over
significant periods of time by attending gatherings and volunteering (e.g.
(Hayes et al., 2019)). Others consulted with community allies or non-profit
organizations to determine appropriate compensation (Freed et al., 2017) and
how to recruit their participants in a privacy-protective way (Guberek et al.,
2018).

Another study used member checking practices, where the researchers shared a
draft of their paper with participants for feedback to ensure they had
accurately represented their experiences and to correct any misunderstandings
(Hayes et al., 2019).

####  6.3.3. Prioritizing Support

In some studies, researchers described how they prioritized the dignity of
participants and supported them during the research process, particularly in
the case of interviews about sensitive experiences. These research choices
included stopping recording interviews when participants became emotional
(Sambasivan et al., 2018), having a therapist present for interviews with
survivors of intimate partner abuse (Leitão, 2019), and appointing same-gender
interviewers for women who have faced abuse (Sambasivan et al., 2019). Beyond
participants, some papers discussed the steps taken to also support the
research teams themselves because of exposure to difficult interview content
(Havron et al., 2019).

###  6.4. How do researchers navigate compensation with marginalized
participants?

Carefully navigating participant compensation is particularly important when
studying marginalized groups, many of whom may be economically disenfranchised
or otherwise vulnerable. While providing participants with compensation
recognizes and respects their time and effort, financial incentives may also
coerce people into participating in the research out of economic need (Ensign,
2003). Thus, whether and how to compensate participants requires careful
consideration and review by institutional ethics boards on a case-by-case
basis (Pater et al., 2021). Reporting and explaining decisions around
compensation can help readers evaluate the research ethics of any given study
(e.g., the potential for undue influence) and its methodology (e.g., the
impact of compensation on recruitment) (Klitzman et al., 2007).

In our dataset, fifteen papers did not involve marginalized populations
directly (e.g., content analyses), and thus the question of compensation was
irrelevant (HCI = 7, C = 4, P = 4). The remaining 73 papers involved some form
of research activity with participants, and we examined these to understand
trends and rationales around compensation; an overview is in Table 3.

Overall, 52% (38) of the 73 user studies reported that participants were
monetarily compensated (HCI = 25, C = 5, P = 8). Compensation ranged from $5
to $100 USD in cash, gift cards, or vouchers, and was typically provided for
interviews. In a few studies, researchers determined appropriate compensation
by consulting with expert organizations and communities (e.g., (Freed et al.,
2017)), which can be a useful strategy when studying marginalized contexts
(Ensign, 2003). In contrast, 41% (30) of the 73 papers provided no information
about compensation (HCI = 21, C = 6, P = 3). In addition, two HCI papers
stated that they did not compensate participants, and three Communication
papers stated that the researchers provided snacks and small gifts in lieu of
financial compensation.

| HCI | Communication | Privacy | Total
---|---|---|---|---
Provided monetary compensation | 25 | 5 | 8 | 38
Provided small gifts or snacks | 0 | 3 | 0 | 3
Explicitly did not provide compensation | 2 | 0 | 0 | 2
Did not report compensation | 21 | 6 | 3 | 30

Table 3. Compensation practices for the 73 studies involving participants

[Table]A table that shows that 38 of 73 user studies provide compensation and
30 do not report compensation; all numbers are also provided in the text of
the paper.

###  6.5. How do researchers navigate positionality?

Researchers’ positionalities—their positions in society based on identity
factors such as class, gender, and race, among others—invariably influence the
research process in several ways, from the types of questions that are asked
to how the work is completed (England, 1994; Parson, 2019). Considering
positionality is particularly important when conducting research with
marginalized groups to ensure the research does not perpetuate the same
marginalization that researchers seek to understand (Parson, 2019).
Positionality can also have implications for researchers’ well-being. Insider
research—when researchers belong to the same group as those being studied—in
marginalized contexts can pose emotional risks for researchers, making self-
care a critical part of such research (Shaw et al., 2020).

Thus, we examined the papers in our dataset to identify whether and how
researchers were engaging with issues of positionality. Overall, positionality
statements were uncommon, with only 23% of papers reporting positionality
across the dataset (HCI = 14, C = 6, P = 0). Positionality statements
typically involved one or more coauthors disclosing that they identified as
belonging to the marginalized population that was the focus of the study. It
was comparatively rare for authors to use these statements to discuss their
relative privilege or non-membership in the groups being studied. Overall,
positionality statements were brief and served to communicate authors’
identities (either marginalized or not), and most papers did not discuss how
these positionalities might inform or impact the research. Some studies in
Communication used a less common practice that is worth noting. In these
studies, when the authors did not belong to the population being studied, they
enlisted population members to help inform the research process in terms of
recruitment, feedback on questions, interviewing, and building rapport with
the community.

##  7\. What are key themes in the findings?

In what follows, we discuss broad themes that reflect the ways in which
marginalization, technology, and privacy were interrelated in the corpus of
papers.  Central to this analysis are the many tensions that arise when people
choose responses to perceived privacy threats, the high-level tradeoffs
involved in these choices, and the specific costs and consequences of
different responses to threats.

Privacy Response |  Cost/Consequence |  Select Examples from the Dataset
---|---|---
Apathy
i.e. lack of response |  • Exposure to risks |  Undocumented immigrants felt government surveillance is inescapable, leading to inaction (Guberek et al., 2018); women transitioning from incarceration felt they have “nothing to lose” (Seo et al., 2020).
Non-use
e.g. not using a technology, deleting an account |  • Opportunity loss
• Exclusion
• Silencing
• Isolation |  Economically disadvantaged populations lose opportunities due to non-use of technologies (Vitak et al., 2018).
Withholding
disclosure
e.g. self-censorship, information removal |  • Restricts
self-expression
• Silencing |  Low-SES youth (Marwick et al., 2017), marginalized Cambodians (Jack et al., 2019), and political refugees in the U.S. (Simko et al., 2018) self-censored to avoid conflict and danger but were further silenced by doing so. Men who have sex with men may not disclose HIV status on dating apps but can inadvertently signal positive status (Warner et al., 2018).
Controlling
disclosure
e.g. compartmentalizing identity, multiple accounts, privacy controls, segmenting audiences |  • Restricts
self-expression
• Labor-intensive
• Social cost
• Financial cost |  Trans men crowdfunding top surgery used privacy controls to limit audiences (Fritz and Gonzales, 2018), young Azerbaijanis maintained multiple accounts for political activism (Pearce et al., 2018), and LGBTQ+ social media users managed identities across platforms (DeVito et al., 2018). Disclosure controls require extensive labor and restrict self-expression (Blackwell et al., 2016). Complex privacy controls can be costly to access (Reichel et al., 2020) and can be used incorrectly due to accessibility issues (Ahmed et al., 2015).
Privacy lies (Sannon et al., 2018)
i.e. providing false information |  • Cognitive burden
• Social/legal repercussions |  Rural Appalachians provided false information as a form of vigilanteism (Hamby et al., 2018); South Asian women provided false information to protect themselves from online abuse (Sambasivan et al., 2019).
Privacy-enhancing technologies (PETS)
e.g., authentication, cloaking, encryption |  • Social liability
• Erasure of records |  Women in patriarchal societies used private modes and locks on devices, which may be seen as incriminating and invite coercion to obtain access (Sambasivan et al., 2018). People who are financially insecure who lose access to trusted devices lose access to services that require two-factor authentication (Sleeper et al., 2019).
Physical workarounds
e.g. hiding device, use of camera covers & headphones |  • Limits environmental awareness
• Vulnerable to physical coercion |  People with visual impairments used headphones to avoid aural eavesdropping when using screen readers at the cost of physical safety (Ahmed et al., 2015).
Asking for help
e.g. learning new practices, consulting network, websites, professionals |  • Bad information
• Involves risk/trust
• Limited to help available |  Professionals who provide support for survivors of intimate partner violence did not feel equipped to advise on identifying or coping with technology-enabled IPV (Freed et al., 2017). People with visual impairments asked allies for help, but this risked trusting the ally with personal information (Hayes et al., 2019).
Collaborative
privacy practices
e.g. shared guidelines, boundaries |  • Loss of autonomy
• Involves risk/trust |  LGBTQ+ adults considered not only their own privacy boundaries but also those of their families, ex-partners, and children (Blackwell et al., 2016). Families co-developed privacy guidelines for shared devices in Bangladesh (Ahmed et al., 2017).
Third-party
protections
e.g. parents removing devices, organizations destroying info |  • Loss of autonomy
• Outside of the person’s control |  Art therapists removed identifying information from art created by persons with dementia to protect their privacy, but this also removed their voice (Cornejo et al., 2016). Canadian government’s legal decision to destroy data documenting colonial abuses of indigenous people to protect their personal privacy also erased evidence of their abuse (Fullenwieder and Molnar, 2018).
Table 4. Privacy Responses and Costs Framework: Types of responses to privacy
threats observed in our dataset and their costs and consequences to
marginalized people.

###  7.1. Responses to Privacy Threats and their Costs and Consequences to
Marginalized People

Many existing privacy frameworks overlook the impact of marginalization on
people’s behaviors (Knijnenburg et al., 2022). In table 4, we present a
Privacy Responses and Costs Framework that lists the ten main types of
responses to privacy threats that we observed in the literature and the costs
and consequences they carry for marginalized people. Research on the privacy
calculus has established that people weigh costs and benefits as they make
choices about privacy (Dienlin and Metzger, 2016); our framework provides an
account of costs and consequences involved in privacy decisions for
marginalized groups. It is also noteworthy that weighing tradeoffs and making
privacy decisions is challenging for people who are under stress (Leitão,
2019; Sleeper et al., 2019; Matthews et al., 2017), adding a new dimension of
complexity to the privacy calculus. Sannon and Cosley characterize privacy
management as a costly form of invisible labor that has an outsized impact on
marginalized groups or those without power (Sannon and Cosley, 2019). In what
follows, we discuss each of these privacy responses and their associated costs
in turn.

####  7.1.1. Apathy.

In some cases, people can feel helpless and may do nothing in the face of
privacy and security threats, a phenomena McDonald describes as “a sense of
futility masquerading as apathy” (McDonald and Forte, 2020). For example, when
exposed to online scams, people from economically disadvantaged communities
may respond with feelings of resignation (Vitak et al., 2018). Similarly,
undocumented immigrants describe the government as an all-knowing entity whose
surveillance is impossible to evade, leading to inaction (Guberek et al.,
2018). The major cost associated with this response is continued exposure to
the disproportionate harms faced by marginalized groups.

####  7.1.2. Non-use.

When faced with poor design and privacy threats, marginalized people may
resist using technologies altogether, which can exacerbate the problem of
exclusion. Economically disadvantaged populations in particular appeared to
resist technology use in our dataset, which could limit their opportunities
for jobs and social support (Hui et al., 2018; Hamby et al., 2018; Sleeper et
al., 2019). For example, Vitak et al. described economically disadvantaged
participants who were “hesitant to use technology or outright shunned it,
preferring to use analog methods for submitting applications, forms, and
payments whenever possible—even when that decision carried additional
financial costs or took longer,” citing a participant who would not apply for
jobs that required online applications (Vitak et al., 2018).

####  7.1.3. Withholding disclosure.

Self-censorship is a commonly discussed strategy for managing perceived risk,
particularly of views that may give rise to interpersonal conflict (Marwick et
al., 2017) or political speech that may have negative repercussions (Simko et
al., 2018; Jack et al., 2019). Marwick et al. observe that “choosing to self-
censor and limit one’s participation is a choice to be rendered invisible”
(Marwick et al., 2017). Self-censorship as a response to privacy threats can
further silence marginalized voices. Moreover, withholding information may not
be effective, with damaging consequences. Warner et al. describe how
withholding HIV status can be ineffective, as people can also make inferences
about undisclosed information (Warner et al., 2018; Warner et al., 2020).
Efforts to withhold disclosure may also be fruitless in the face of
intentional adversarial threats like the use of stylometry to identify content
creators (Forte et al., 2017).

####  7.1.4. Controlling disclosure.

Social media studies made up 26% of the dataset, and a common privacy-
protective behavior, especially on social media, centered around people making
efforts to compartmentalize their identity or choose their self-presentation
in ways that would allow them to pursue their goals while shielding them from
risk. Some ways to do this are to use privacy controls to restrict access to
one’s information, to open multiple accounts on the same platform to keep
various facets of one’s identity separate (Haimson et al., 2015), and to
select platforms based on the degree of privacy they afford (Karusala et al.,
2019; DeVito et al., 2018). However, extensively locking down one’s online
profile also means that one’s ability to express one’s identity is restricted
(DeVito et al., 2018). The labor involved in controlling disclosure is also
considerable. Fritz and Gonzales describe how one trans participant who was
crowdfunding surgery “took an entire day to go through all his Facebook
connections and block approximately 600 people who were connected to his
family so they would not see when he promoted his fundraiser on Facebook”
(Fritz and Gonzales, 2018). Pearce et al. describe the labor Azerbaijani young
adults expend to segment audiences for political posts: “those with two
profiles engaged in a great deal of labor to manage the two—defriending people
on one, adding them to the other, inventing innocuous reasons why a new
profile was created, and so on” (Pearce et al., 2018), and additionally
describe the social cost of dissolving online ties to control disclosure. In a
study of South African mobile privacy practices, Reichel et al. explain that
using of privacy controls carried a financial cost associated with
connectivity: “Nearly every time a participant expressed awareness of these
privacy settings, they followed by explaining they had an inability to
actually access the privacy settings, often mentioning data costs” (Reichel et
al., 2020).

####  7.1.5. Privacy Lies.

Providing false personal information about oneself by telling “privacy lies”
(Sannon et al., 2018) appeared several times as a privacy strategy to deter
threats. Sambasivan et al. describe the use of fake information as a strategy
for South Asian women to protect themselves from abuse online (Sambasivan et
al., 2019). Hamby et al. frame providing false information as a kind of
privacy vigilanteism in rural Appalachia (Hamby et al., 2018). However,
telling privacy lies requires cognitive effort to ensure the lies are not
found out, and can also be risky, as being caught out in a lie can result in
social or legal repercussions (Sannon et al., 2018).

####  7.1.6. Privacy-enhancing technologies (PETS)

Papers in our dataset reported on a limited range of technological responses
by marginalized groups to privacy threats. These largely involved private
modes, encryption, and locking/adding additional authentication requirements.
Despite the protections they offer, the use of PETs may incriminate the very
people who need protection. Sambasivan et al. report in a study of women in
India, Pakistan, and Bangladesh that “private modes are often associated with
‘secret’ activities, threatening participants’ values of openness as they
performed culturally appropriate gender roles” (Sambasivan et al., 2018).
Similarly Ahmed et al. observed in a study of shared mobile phone use in
Bangladesh that “almost half of our participants reported that locking
specific data or applications might also raise suspicions in the mind of their
partners” (Ahmed et al., 2017). Locking down devices and applications may also
result in further coercion or physical harm in the context of intimate partner
violence (Leitão, 2019; Freed et al., 2018). Because of this cost, hiding the
fact that privacy protections exist was surfaced as a design recommendation in
multiple papers. Naseem et al. quoted a participant in Pakistan who applauded
the design of secret PETs, “That way, at least one won’t come across as
suspicious, especially since men in our society are very distrustful and
suspicious” (Naseem et al., 2020). However, leaving no trace of activity
carries additional consequences for people in abusive relationships in that it
eliminates documentation of abusive behaviors (Ramirez and Lane, 2019).
Additionally, the use of two-factor authentication can contribute to a
victim’s powerlessness if the victim loses access to devices required to
authenticate, possibly due to interference by the abuser (Sleeper et al.,
2019).

####  7.1.7. Physical workarounds.

Hiding devices, covering cameras, using headphones to prevent others from
overhearing screenreaders–these are all documented physical workarounds that
members of marginalized groups have to privacy threats. Ahmed notes that many
visually impaired participants used headphones to ensure privacy when using
screenreaders; however, “since visually impaired people rely on hearing in
order to sense the environment, headphones leave them more vulnerable to other
privacy and safety concerns” (Ahmed et al., 2015); thus the headphones might
protect their information privacy at a cost to their physical safety. People
in abusive relationships may also physically hide their devices to avoid
unwanted snooping (Matthews et al., 2017), but hiding devices comes with many
of the same threats of physical or emotional coercion described in the above
section on PETs.

####  7.1.8. Asking for help.

Asking for help may involve learning new privacy practices from social
contacts, or consulting privacy resources and professionals. When people rely
on their social network, a practice found to be more common among lower SES
individuals (Redmiles et al., 2017), the help they receive is only as good as
the knowledge in their network. Poor advice can carry a high cost for those in
vulnerable positions and even trusted professionals may not be sure what to
advise. For example, professionals who support survivors of intimate partner
violence don’t always know what to advise (Leitão, 2019). Receiving help from
others can also entail divulging sensitive information to third parties, which
requires trust and can introduce a new privacy risk (Vitak et al., 2018;
Mentis et al., 2020; Simko et al., 2018).

####  7.1.9. Collaborative privacy practices.

Collaborative practices sometimes arise as a response to concerns about
privacy threats within family units or other relationships. Practices like
establishing shared rules and boundaries (Ahmed et al., 2017) provide a social
alternative to PETs like app locks and encryption, but require high levels of
trust and may compromise autonomous decision-making.

####  7.1.10. Third-party protections

In literature about marginalized populations with heightened vulnerabilities,
it is unsurprising to find examples of third parties with power making
decisions that impact marginalized groups. Examples in our dataset range from
husbands making decisions for wives in patriarchal societies (Karusala et al.,
2019), governments taking action on behalf of vulnerable groups (Fullenwieder
and Molnar, 2018), and families and therapists taking responsibility for
adults with cognitive decline (Cornejo et al., 2016; Müller et al., 2010).
Costs of third-party protections include a loss of autonomy and control; when
others make privacy decisions, the line between helpful and paternalistic can
be difficult to see. Mentis et al. explore this tension in work with adults
with cognitive decline and their partners (Mentis et al., 2020); they find
that although partners aim to negotiate security decisions, the reality is
often that decision-making ends up being one-sided by the caregiver partner.

###  7.2. High-level privacy-related tensions and trade-offs for marginalized
technology users.

In addition to the granular costs and consequences described above, we
identified several high-level privacy-related tensions in studies of
marginalized groups’ technology use:

####  7.2.1. Privacy vs. disclosure of identity

Most uses of technology entail some degree of identity disclosure, ranging
from highly visible disclosures like creating a personal account with legal
identifiers, to less visible disclosures like inadvertently sharing location
via network data. For marginalized groups, the privacy threats associated with
everyday use may be acute, even if the groups themselves are not cognizant of
the threats (Guberek et al., 2018). In some cases, technologies create unique
avenues for sharing among marginalized groups that come with elevated threats.
For example, LGBTQ social media users may use platforms to connect and explore
their identity (Haimson et al., 2020) but also risk stigma from unintended
audiences if their privacy strategies are compromised (DeVito et al., 2018).
Marginalized groups engaging in activism (Lerner et al., 2020) or
participating in online collaborations (Forte et al., 2017) may disclose
identity characteristics by virtue of these activities. In other cases,
privacy breaches can be the result of secondary data use, for example by
analyzing meetup data to infer LGBTQ identity (Chung et al., 2017) or social
media posts to infer mental health status (Weerasinghe et al., 2019).

####  7.2.2. Privacy vs. support

Because marginalized groups by definition experience some form of stress,
support seeking is particularly salient. Risking disclosure of vulnerabilities
to receive support is not unique to technology-mediated contexts (e.g.
(Guberek et al., 2018)), but technologies can exacerbate the privacy threats
associated with support seeking. Srinivasan et al. (2018) observe in their
research on identity infrastructures in India that low income and marginalized
people often “are the people who most need benefits from the state, and to
receive benefits, they must identify themselves,” which can introduce risk of
stigma and persecution. In a different context, strikingly similar trade-offs
arise: for trans men who crowdfund financial support for top surgery, support-
seeking can entail highly public disclosures (Gonzales and Fritz, 2017; Fritz
and Gonzales, 2018). Sambasivan et al. investigated the use of a phone
messaging system intended to help deploy social and health services to urban
sex workers who are vulnerable to physical violence and health issues, but for
whom receiving such support could mean stigma or further physical threat
(Sambasivan et al., 2011).

####  7.2.3. Privacy vs. autonomy

Privacy is an important way of protecting individuals’ freedoms (see (Westin,
1968)). As such, oversight of others’ location, well-being, or activities is
often paternalistic or invasive, but some of the research in our dataset
discussed scenarios in which forms of surveillance were used to facilitate
autonomy. One example of the challenges of navigating privacy and autonomy
arises in the work of victim service providers (VSPs). These organizations
support survivors of human trafficking. In order to help ensure that survivors
of human trafficking are not revictimized and in a direct bid to protect
survivors’ freedoms, VSP shelters may surveil communications, particularly of
minors, to enforce rules and monitor for risky behaviors (Chen et al., 2019).
Similarly, Mentis et al. identified threats, tradeoffs, and design
considerations with respect to privacy of older adults with cognitive decline
and their caretakers (Mentis et al., 2019); they note that while collaboration
between adults with cognitive decline and caregivers is critical, supporting
collaboration entails privacy concessions that can create opportunities for
exploitation. Similarly, location tracking can be viewed as a safety measure
that supports adults with dementia in retaining more freedom (Müller et al.,
2010), but it can also be subject to potential abuse (Dahl and Holbø, 2012).
This handful of papers highlight important boundaries where social norms and
cultural standards around the limits of privacy and acceptable privacy trade-
offs for safety and wellbeing are negotiated.

####  7.2.4. Individual vs. collective

Although privacy theorists often weigh the interests of the individual against
those of states and organizations (see (Westin, 1968; Brandeis and Warren,
1890) for foundational examples in Western culture), in some cases, privacy
concerns are collective. That is, more than one person may work together to
protect the shared privacy interests of a group. The literature on
marginalization and privacy included several examples of such framing. For
example, LGBTQ+ parents on social media find that their privacy depends not
only on their own disclosure decisions but also the disclosures and behaviors
of their network. Further, their self-disclosures can also impact the privacy
boundaries of those who make up their network, such as their children,
partners, and former partners, and potentially expose the network to
stigmatization as well (Blackwell et al., 2016). In some cases, additional
help may be needed in navigating privacy—for example, in the context of
patients with dementia, Cornejo et al. describe how negotiating privacy is a
process that is shared between the patients, family members, and care
professionals (Cornejo et al., 2016). People’s privacy preferences may also be
“socially-negotiated” rather than purely their own, as with people managing
bipolar disorder whose family members and care team who would like them to
regularly share their personal data in the form of a “check-in” (Petelka et
al., 2020).

###  7.3. Technologies and the way they handle privacy can either contribute
to or impede the process of marginalization.

We identified two common narratives across the literature that were connected
to the kind of mediating role that technologies can have as the relationship
between privacy and marginalization plays out. On one hand, technologies can
be viewed as a mechanism for greater equity and freedom, slowing or perhaps
optimistically even reversing the process of marginalization for certain
groups, such as technologies that provide assistive support (Wang et al.,
2019) or safe spaces for disclosure and support seeking (e.g., (Hong et al.,
2012; Carrasco and Kerne, 2018; Naseem et al., 2020)).

On the other hand, technology can be viewed as a mechanism that supports and
strengthens processes of marginalization by disproportionately introducing
privacy threats and harms or furthering exclusion. In some cases, the design
of technologies is insufficient to protect the privacy interests of
marginalized groups. For example, insufficient control over disclosures of
HIV-positive status in dating apps can lead to “privacy
unraveling”—particularly for men who have sex with men—which can further
exacerbate their marginalized status (Warner et al., 2018). Being from a
marginalized group means that technologies may be designed in ways that
exclude people from their use at all, as Rennie et al. explain, “social
dynamics and obligations can prevent Aboriginal people from using devices and
settings in the way they were intended” (Rennie et al., 2018). Being prevented
from using technology by virtue of poor design is yet another form of
exclusion that marginalized groups experience. In some cases, poor design or
the threat of privacy breaches may lead to non-use or resistance (Hamby et
al., 2018) which, while sometimes framed as a form of agency and empowerment,
can also result in exclusion from digital public spaces (Forte et al., 2017).
Reichel goes further to examine how the very concept of privacy as a goal is
embedded within and therefore yields social and technological structures that
exclude marginalized groups (Reichel, 2017) and Gangadharan (2017) notes that
inclusion efforts intended to assist marginalized groups may expose them to
greater threats of surveillance and risk.

##  8\. What are key themes in the papers’ recommendations?

We organized the recommendations in the papers into three broad
categories—conceptual, technological, and behavioral recommendations—that we
discuss next.

###  8.1. Conceptual recommendations

####  8.1.1. Prioritizing autonomy and dignity in design

Several authors recommend rethinking the ways we talk about privacy and the
concepts we use to understand and design privacy for marginalized groups.
Akter et al. call for “humanizing” assistive technologies specifically because
they found that “camera-based assistive systems were creating a lack of
security in people’s daily lives—that is, these systems were serving to
further marginalize their identities” (Akter et al., 2020). Reichel rejects
the concept of privacy, seeing it as inherently flawed in that it requires
identification of a threatening “other,” and advocates instead to organize
what is now privacy discourse around the more fraternal concept of dignity
(Reichel, 2017). Fullenwieder and Molnar (2018) similarly critique the notion
of privacy as an individual right in their analysis of how privacy was
leveraged to support the destruction of testimony about state-sanctioned
violence against indigenous populations in Canada.

Designing technologies that do not threaten the privacy of marginalized groups
also involves assessing which values are given importance in the design
process, and considering how these values might impact marginalized groups.
Values can be in conflict: for example, remote monitoring systems for people
with dementia can prioritize the value of keeping them safe over preserving
their privacy. In light of such conflicts, Dahl and Holbø recommend that value
elicitation should be a necessary part of designing in such settings to
evaluate technological biases and impacts (Dahl and Holbø, 2012). Similarly,
Wan et al. argue that flexibility should be built into technologies so that
they can be adapted to fit potentially diverse needs in different
organizational and family contexts (Wan et al., 2014).

Prioritizing people’s agency is one way of ensuring technologies do not harm
marginalized groups. For example, automatic gender recognition software can
engender numerous harms for transgender people; Hamidi et al. caution
designers to consider whether gendering users is truly necessary and to
exclude gender from their design if possible. In cases where such technologies
are used, they recommend providing users with the agency to opt out of being
gendered (Hamidi et al., 2018).

####  8.1.2. Recognizing the influence of power relations on technology use

Studying marginalization means studying power. As part of their focus on
marginalized groups, many researchers call for designers “to consider the ways
that technologies may not be one-size-fits-all” (Fernandez and Birnholtz,
2019). These researchers highlight the need for designers to pay more
attention to how power relations structure the ways marginalized groups use
technology and to tailor technologies accordingly. For example, locking a
mobile phone may not be effective in a heavily patriarchal context where a
woman can be coerced by her husband to unlock it (Ahmed et al., 2017). Similar
dynamics may occur due to familial and sociocultural power relations,
especially in contexts where it is the norm for multiple people to share the
same device (Sambasivan et al., 2011), such as in South Asia (Sambasivan et
al., 2019). Conducting digital risk assessments for technologies would also
identify privacy risks and gaps in privacy protection based on different use
cases, such as assessing if certain technologies pose risks to survivors of
intimate partner violence, given their unique threat model where they are
avoiding a known other (Freed et al., 2017).

###  8.2. Technological recommendations

Most recommendations, particularly in HCI, centered around how technologies
can be designed to better address the unique privacy needs and concerns of
marginalized groups.

####  8.2.1. Providing greater control over information

Since many marginalized populations can face heightened risks from having
their privacy violated, many studies stressed the need to afford people with
greater control over their information (e.g., (Haimson et al., 2015; Gonzales
and Fritz, 2017; Carrasco and Kerne, 2018; Fernandez and Birnholtz, 2019)). To
this end, a common design suggestion was that technologies should have
granular privacy settings so that users can better control the visibility of
their content, thereby avoiding the risks of context collapse. For example,
Carrasco et al. discuss how LGBTQ+ social media users practice “selective
visibility” by being more “out” to LGBTQ+ audiences but not cisgender or
heterosexual audiences; they suggest that social media platforms that
facilitate this form of selective sharing—for example, through supporting the
use of multiple profiles—would give marginalized social media users more
agency over their self-presentation (and by extension, their privacy and
safety) (Carrasco and Kerne, 2018).

Designing technologies that do not diminish the privacy of marginalized groups
also requires designers to consider the “labor and risk involved in conveying
sensitive information about the self to others”: for example, some groups—such
as trans individuals—may not want to seen by the broadest audience possible
when using online dating platforms, as this could open them up to harassment,
and instead would benefit from controlling the visibility of their profiles
(Fernandez and Birnholtz, 2019).

####  8.2.2. Facilitating management of communal and networked aspects of
privacy

Several papers stressed the need for designers to consider communal aspects of
privacy. In addition to managing their personal privacy boundaries, people
often have to navigate collective boundaries as well, and granular privacy
controls on online platforms would help such networked privacy management
(Blackwell et al., 2016). In cases where multiple stakeholders jointly
negotiate privacy, such as in the case of people with dementia and caregivers,
systems could support cooperatively setting privacy preferences (Cornejo et
al., 2016).

People can also face privacy threats from others who have access to their
devices, either with or without their consent. In such cases, a common design
recommendation is to facilitate secrecy and the hiding of sensitive
information. For example, mobile phones could be set up to allow an individual
to hold multiple accounts that are kept secret if others access the device
(Ahmed et al., 2017). Similarly, enabling multiple users to create individual
accounts on shared devices also helps maintain each individual’s privacy
(Leitão, 2019). Devices could also enable on-demand information “hiding”, a
feature that would be especially useful for groups who could face severe
consequences if their information were accessed by the wrong parties, as in
the case of undocumented Latinx immigrants (Guberek et al., 2018).

####  8.2.3. Making privacy decisions easier

Another type of recommendation centered around making it easier for
marginalized groups—and people in general—to make informed privacy-related
decisions. One way of doing this is to improve the usability of privacy and
security features and settings, including when people are under dire stress,
as in the case of avoiding intimate partner abuse (Matthews et al., 2017).
Another option is to design for transparency, so that they have more clarity
around when their information is being collected and how it might be used.
Sometimes this transparency is important with respect to how data will be used
by social media companies and service providers (Cabañas et al., 2018), but
also with respect to how information is shared with other parties. For
example, in the context of an app that helps people with bipolar disorder
continuously disclose mental health information to trusted others, managing
this continuous sharing in a sensitive way requires interfaces to clearly
indicate who can see what data at any given time (Petelka et al., 2020).

####  8.2.4. Building in technical safeguards

Whereas many of the above-discussed technical recommendations hinged on user
experience and interface features, several papers also discussed how technical
infrastructures could be better designed to safeguard marginalized
populations. For example, in the call for humanizing assistive technologies,
Akter et al. (2020) note that computer vision algorithms must be designed to
detect not only objects, but features of context that matter to the humans who
use them. Systems that provide infrastructure for anonymous online activity
can protect people with marginalized identities in a variety of contexts, for
example, survivors of abuse who wish to report abusers (Chen et al., 2019).

###  8.3. Behavioral recommendations

Some researchers stressed the need to help people engage in behaviors that
keep them safe and align with their privacy needs. One way to do this is to
provide people with education and resources. Access to privacy-related
education can be particularly useful for groups such as undocumented
immigrants, who may not be aware of the intricacies of the technological
privacy and security threats they face (Guberek et al., 2018). Educating
people about how to protect their privacy can help them continue to use
technologies while mitigating the risks in doing so, as in the case of
contributors to open collaboration projects like Wikipedia who have
marginalized attributes (Forte et al., 2017). In both these studies, the
authors stress the need for such social interventions to occur in conjunction
with technological solutions that focus on improving the technologies
themselves.

In some cases, marginalized groups may be well aware of the fact that they
face privacy and security threats, but feel limited in their ability to
address these threats. Research on intimate partner abuse suggests that
survivors need targeted instructional materials that will help them avoid
their abusers, such as information on how to use security features like two-
factor authentication (Matthews et al., 2017). In response to this need for
access to resources, two studies in our dataset explored the usefulness of
providing survivors of intimate partner violence with security consultations
with a trained technologist, finding that these consultations were generally
perceived as valuable and also uncovered security vulnerabilities that
participants were not aware of (Freed et al., 2019; Havron et al., 2019).

Despite multiple studies pointing to a need for improved access to privacy
education, a challenge in improving privacy education among marginalized
groups is that digital literacy programs are not always well-attended; in
response to this challenge, Vitak et al. point out that stories are often an
effective means of spreading information in low-income communities and could
be a way of sharing privacy-related knowledge and resources (Vitak et al.,
2018). Reichel et al. suggest that “lightweight privacy on-boarding
interfaces” could help resource-constrained users, including making privacy
settings available offline for those who have limited connectivity (Reichel et
al., 2020). People may also be able to improve their digital privacy skills if
they have unrestricted, private access to the Internet, but many disadvantaged
communities rely on libraries and schools to access the Internet where their
usage is time-limited; thus, addressing the digital divide remains crucial to
increasing both the autonomy and privacy literacy of marginalized groups (Li
et al., 2018).

##  9\. Where do we go next? Charting Future Directions

Our initial dataset included 2,823 privacy-related papers that were published
between 2010 and 2020; of these, only 3% (88) focused on marginalized
contexts. Although our dataset does not include all venues where such work
might appear, this highlights how profoundly understudied this area of
research remains. We also found that research in this area is growing over
time, and we end this paper by discussing some potential avenues for future
research in terms of focus, topic area, methods, and research practices.

###  9.1. Broaden How We Problematize Privacy and Marginalization

Although we identified several common themes that cut across studies, papers
in our dataset did not clearly converge on a shared articulation of problems
or solutions. We note that, although all the papers examined marginalized
groups that are, by definition, marginalized as a function of social norms and
structural inequities, only a handful of papers problematized privacy in the
context of social structures, policies and laws. We are confident that nearly
all researchers doing work on privacy at the margins understand that
technologies, policies, and social structures intersect and inform one
another; however, efforts to examine these intersections were rarely central
features of papers. In contrast, empirical studies, which were the most common
type of contribution in our dataset, tended to focus on people’s experiences
and behavior. These studies mark an important shift towards representing the
voices of users who have been traditionally excluded from technology research.
Alongside these much-needed contributions, we suggest that researchers
leverage and build on critical lenses that highlight structural and systemic
aspects of marginalization that underlie technology design and use. Correcting
inequities in privacy is also a problem with potential policy implications.
Our dataset yielded few if any policy recommendations, and we see a need for
more interdisciplinary work that bridges technology design and policy.

###  9.2. Fill Understudied Research Gaps

While research on marginalization and privacy is growing rapidly, particularly
in the past three years, this growth has not been uniform across research
areas. As we coded our dataset of existing literature, it was quickly apparent
that some research topics at the intersection of privacy and marginalization
remain undeveloped.

The paucity of discussion around race and privacy in our dataset was
conspicuous. Papers that examined issues of race and power as their central
focus were few, and mainly within the Communication dataset (Reichel, 2017;
Fullenwieder and Molnar, 2018). Additionally, some papers looked at contexts
in which race was inextricable from the context under study, such as refugees
and other immigrants, but for the most part, these did not explicitly reflect
on the influence of race in the experience of marginalization. Additionally,
race was sometimes mentioned as a factor to consider in contexts like crime
prevention or low-income neighborhoods, but was not used as a lens through
which to understand privacy concerns or as an analytical tool in empirical
work. This is surprising for HCI in particular, given a trend toward
considering technologies as potential instruments of oppression and
marginalization and increasing attention to conceptual frames that address
features of race, like critical race theory (Ogbonnaya-Ogburu et al., 2020)
and—at times controversially—intersectionality (Rankin and Thomas, 2019).
Although such frames were at times mentioned in discussion, they were
generally absent from the body of empirical work we reviewed. To fill this
important gap in the literature, we see a dire need for privacy
research—particularly within HCI and Privacy-focused venues—that includes and
centers race.

It is worth noting that this is a new subfield; even the most common topics
are still relatively understudied and remain rich areas for further
exploration. This is also true within various topic areas. For example,
disability and LGBTQ+ issues were the two most common topics and together made
up almost half of our dataset, but certain subpopulations were less studied
than others, such as people with mental health conditions (n = 3) and queer
women (n = 1), respectively. As such, our review illustrates the current
breadth of work in this space but also shows that there is much room for
future work across topics.

In order to fill these gaps, we see an immediate opportunity for conferences
and journals to foster research in this area. Scholarly leadership can support
these efforts by mentioning topics related to marginalization in CfPs,
dedicating special issues, panels, and workshops, identifying keynotes and
themes. Moreover, program committee members and reviewers must recognize the
value of studying marginalized groups independent of comparisons to or in the
interests of designing for the majority population.

###  9.3. Diversify Methods and Questions

Qualitative methods—particularly semi-structured interviews—were by far the
most common methods used by researchers. This makes sense given that the goal
of much empirical work thus far has been to understand the privacy-related
needs and experiences of marginalized user groups. While these methods are
likely common because they are the most appropriate for the types of questions
the community has been asking, our findings indicate the potential to leverage
other methodologies to complement and build on existing research questions.
Within the umbrella of qualitative research, ethnographies, textual analyses,
and case studies are all examples of methods that are under-represented in
this space but may still be well-suited to the questions being posed. There is
also potential to leverage quantitative methods to a greater degree; for
example, to measure privacy inequities and disparities, or to experimentally
test technology designs intended to mitigate these disparities. Further, a
minority of papers used participatory methods to co-design privacy-aware
technologies with users, and the privacy community may benefit from greater
use of these methods, particularly as mechanisms by which to involve
marginalized people in design. While it is important for researchers to
acknowledge that new tools may not be wanted or needed by the individuals they
intend to serve, the few participatory studies in our dataset suggest that
there is potential to work in conjunction with marginalized communities to co-
develop tools or re-design existing technologies to better meet their needs
and practices. To do so, we can also look to HCI research on other
marginalized contexts, such as participatory action research in under-
resourced neighborhoods (Hui et al., 2020), for guidance.

###  9.4. Develop Shared Best Practices

We found wide variation in both research and reporting practices across
studies, such as the amount of detail provided in methods sections and
decisions around ethical considerations, such as compensation. Particularly in
the context of studying marginalization, we see an urgent need to consider,
justify, and report study details, ranging from why a given recruitment
strategy was chosen to potential harms from the research and how these were
mitigated. These are challenging aspects of study design, and reporting the
decision process underlying these research choices is important for research
communities to engage in discussion/critique and to develop shared practices
and ethical standards.

Here, we discuss some research considerations that researchers need to reflect
on when crafting and conducting our studies and report on when publishing our
papers as a first step towards developing shared best practices for studying
privacy and marginalization.

####  9.4.1. Plan for and reduce a wide range of potential harms

Reducing harm and maximizing benefits are central tenets of international
standards for ethical research (Code, 1949; for the Protection of Human
Subjects of Biomedical and Research, 1979). Categories of “vulnerable”
populations highlighted by bodies like Institutional Review Boards (IRBs)—for
example, prisoners, pregnant women, children—do not cover all possible at-risk
groups. Marginalization by definition creates vulnerabilities. When working
with marginalized groups, researchers need to take particular care to
understand the potential risks involved in their participation. Whereas a
large portion of our data corpus was focused on identifying privacy risks,
these papers rarely discussed the privacy risks and precautions related to
study design beyond anonymizing data or using pseudonyms. According to a
review of anonymization practices at CHI, this is not atypical (Abbott et al.,
2019). Moreover, some study designs in our corpus may have introduced novel
risks to the population, for example through automated detection of
marginalized identity features, without a robust discussion of the ethical
implications of such research endeavors. As a research community it is
important not only to carefully consider, but also unambiguously report our
considerations of potential harms or unintended consequences and how these
risks are mitigated in study designs.

Another important consideration to reduce harms in research is in the language
that researchers choose to represent marginalized people and experiences.
Language that others, diminishes, or inadvertently stigmatizes marginalized
groups can introduce harms through the research itself. Researchers should
take care to learn and discuss the preferences of the groups involved in
research and report if and how specific choices were made around labels and
terminology where controversy or disagreement exist.

Other strategies used to mitigate harm in our dataset included having a
therapist on hand to assist if interview participants experienced distress
(Leitão, 2019), along with other strategies already discussed in Section 6.3.
Researchers who conduct interviews on sensitive topics or with groups facing
adversity may also find Kasket’s (Kasket, 2009) protocol for responding to
participant distress useful.

Finally, while studying marginalization, it is also worth considering
potential harms to researchers themselves. Researchers can face burnout as
well as physical safety risks when conducting research on sensitive topics
(Ensign, 2003). A handful of papers in our dataset discussed the steps that
were taken to ensure researcher well-being, and these discussions were useful
to understand some of the challenges that may surface when conducting research
in this space, as well as how these risks might impact the research process
and the scope of the findings. For example, a study in our dataset that
involved conducting interviews in unsafe neighborhoods noted that the
interviews were strictly time-bound to ensure the safety of the researchers,
sometimes at the cost of asking additional follow-up questions (Reichel et
al., 2020).

####  9.4.2. Report compensation

A review of HCI papers published by the ACM found that the majority of user
studies did not report whether participants were compensated (Pater et al.,
2021). Our dataset suggests that researchers working at the intersection of
privacy and marginalization may be more likely to report this, but still,
about half of the user studies in our dataset did not include this
information.

We echo Pater et al.’s call for researchers to report this information,
including their rationales. When providing compensation, researchers could
consider whether there are implicit biases in their compensation choices—for
example, not all participants may want an Amazon gift card (Pater et al.,
2021), and in the context of studying marginalized groups, there may be
differences in access that make some forms of compensation more appropriate or
desirable than others. A few studies in our dataset described consulting with
community members or partners to decide on appropriate compensation, which is
a practice that could be particularly well suited for researchers in this
space to adopt.

####  9.4.3. Discuss positionality

Positionality is an important feature of research—researchers’ identities,
beliefs, experiences, and backgrounds directly inform their selection of
methods, engagement with participants in the case of human-subjects research,
and interpretations of data. How to go about reporting positionality is a
complex decision—for example, positionality statements that involve identity
self-disclosures may disproportionately harm marginalized researchers. We do
not make prescriptive statements about whether all studies should explicitly
include this information or not. However, considering positionality is a vital
part of the research process, particularly in the context of working with
marginalized groups. In our dataset, a minority of papers reported
positionality, often in the form of disclosing researchers’ own marginalized
identities. We suggest, echoing Liang et al. (2021), that positionality
statements need not always include identity characteristics. Positionality
statements do not confer legitimacy, and although they may provide useful
information about researchers’ insider status within a community of study, the
goal of a positionality statement is to provide context that helps readers
understand the research being presented. In some cases, researchers’ political
beliefs, epistemological commitments, and disciplinary training may be more
helpful in understanding how research was conducted than specific identity
characteristics. For example, in the case of this paper, both authors have
experienced marginalization in multiple dimensions, which may help readers
contextualize our interest in and commitment to this topic, and we are both
privacy researchers who approach research from a critical, interpretivist
perspective. These are important things to know about us, since we are
offering research advice, whereas specific disclosures around our race, sexual
orientation, and other identity features may not be.

###  9.5. Limitations and Future Work

Literature reviews have limitations that revolve around the selection of
conferences and databases, as well as the criteria used to search for and
filter papers.

First, our sample was scoped to a large but still limited set of venues. This
is because our sampling strategy was designed to uncover themes in a broad,
interdisciplinary set of papers at the intersection of privacy and
marginalization, rather than to identify a comprehensive dataset of all papers
published in this space across fields. As a result, we limited our search to
SIGCHI-sponsored venues, select Communication journals, and select venues that
focus on privacy and security research. While our exploratory searches
suggested that these were some of the most common spaces for research relevant
to our focus, it also constrained our sample. We think there is potential for
future work to examine other conferences and fields in greater detail,
including venues that focus on historically marginalized groups, such as the
ACM Conference on Computers and Accessibility (ASSETS).

Second, the criteria we used to search for and filter papers also influenced
our sample. When searching for relevant papers in ACM SIGCHI-sponsored venues
and Communication journals, we constrained our search to just one keyword
(“privacy”). This allowed us to limit the size of our dataset and remain
consistent across subfields; however, in doing so, we may have missed work on
marginalization as it intersects with other topics related to privacy, such as
trust and disclosure. Since we filtered papers based on whether they contained
the search term “privacy” in the title, abstract, and/or keywords, we likely
missed some relevant papers that are not focused on privacy but nevertheless
have interesting privacy-related findings pertinent to our focus.

Further, “marginalization” is a term with blurry boundaries and multiple
definitions that are open to interpretation. Manually filtering our dataset to
identify papers on marginalization required us to draw on several definitions
of marginalization that often differed from each other, as well as our
conceptualizations that were necessarily constrained by our own readings and
positionalities. Our initial step was to read widely about how the concept of
marginalization has been developed and used in a variety of fields, and we
engaged in ongoing discussions about marginalization within our research
group. Ultimately, our decision about whether to include studies in our
dataset was informed by our understanding of what different social groups
experience, our interpretations of definitions, and colored by our own beliefs
and backgrounds. While we chose to focus on the experiences of marginalized
adults, we also see potential for future work to explore research on children,
who are a vulnerable population with unique privacy risks.

##  10\. Conclusion

The goal of this paper was to serve as a roadmap for current and prospective
researchers working at the intersection of privacy and marginalization by
providing a review of current knowledge in the field, the practices through
which it has been generated, and to chart a way forward. Our review of 88
papers published between 2010–2020 in HCI, Communication, and Privacy-focused
venues found that this is a fast-growing area of study with wide variation in
topics and research practices.  Many existing privacy frameworks do not
account for marginalization (Knijnenburg et al., 2022), and in response, we
introduced the Privacy Responses and Costs framework to reflect the range of
privacy responses people enact and the costs and consequences of these
responses to marginalized groups.

We also uncovered topics that need further study, such as race, the structural
aspects of marginalization, and the role of policy, as well as the potential
to use more diverse methods in our practices, including quantitative and
participatory methods. Finally, given our focus on marginalized groups, we see
a need to discuss and report research practices in greater detail,
particularly around the ethical considerations of our work, and we put forth
some suggestions for establishing shared best practices to do so.

###### Acknowledgements.

The authors thank Mako Hill, Rachel Greenstadt, and Kaylea Champion for their
feedback throughout this study. The work was supported by the National Science
Foundation (Award #2031951). The first author was also supported by the NSF
under Grant #2127309 to the Computing Research Association for the CIFellows
Project. The findings and recommendations in this paper are those of the
authors and do not necessarily reflect the views of the NSF or the CRA.

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