# Domain Aligned Prefix Averaging for Domain Generalization in Abstractive
Summarization

Pranav Ajit Nair {pranavajitnair.cse18<EMAIL_ADDRESS>Sukomal Pal
{pranavajitnair.cse18<EMAIL_ADDRESS>Pradeepika Verma
<EMAIL_ADDRESS>
###### Abstract

Domain generalization is hitherto an underexplored area applied in abstractive
summarization. Moreover, most existing works on domain generalization have
sophisticated training algorithms. In this paper, we propose a lightweight,
weight averaging based, Domain Aligned Prefix Averaging approach to domain
generalization for abstractive summarization. Given a number of source
domains, our method first trains a prefix for each one of them. These source
prefixes generate summaries for a small number of target domain documents. The
similarity of the generated summaries to their corresponding documents is used
for calculating weights required to average source prefixes. In DAPA, prefix
tuning allows for lightweight finetuning, and weight averaging allows for the
computationally efficient addition of new source domains. When evaluated on
four diverse summarization domains, DAPA shows comparable or better
performance against the baselines, demonstrating the effectiveness of its
prefix averaging scheme111https://github.com/pranavajitnair/DAPA.

##  1 Introduction

Abstractive document summarization aims at filtering the most crucial
information in a document to present a concise view of it (Nallapati et al.,
2016; See et al., 2017). This document may take the form of a news article
(Hermann et al., 2015), a scientific paper (Yasunaga et al., 2019), a dialogue
(Gliwa et al., 2019), or a social media post (Kim et al., 2018). The advent of
pretrained models (Raffel et al., 2020; Lewis et al., 2020) has significantly
improved abstractive summarization on several of the aforementioned domains.
However, these approaches require extensive manual labelling of data which
limits their use to domains without any labelled data. Given that real-world
applications of summarization often face the problem of adapting to new
domains, it becomes crucial to develop summarization systems that do well in
data-scarce settings by leveraging information from the source domains.

Domain generalization accounts for learning a robust model for unseen domains
from a set of source domains. This problem is closely related to transfer
learning, multitask learning and domain adaptation all of which involve
learning a model from a set of source tasks/domains to perform well on a set
of target tasks/domains. However, in the case of domain generalization,
labelled data for the target domain is unavailable. Previous works on domain
generalization mainly focus on learning domain invariant features (Gulrajani
and Lopez-Paz, 2020; Wang, 2020; Li et al., 2018). Such methods work well on
classification tasks where learning domain invariant features is sufficient to
predict target classes. However, they may be insufficient for language
generation tasks which have writing style, grammar as their ingredients (Vu et
al., 2022). Moreover, such methods involve sophisticated algorithms for
training and cannot be used for lightweight domain generalization.

Prefix tuning (Li and Liang, 2021) is a lightweight approach to adapting
pretrained language models to downstream tasks. It augments the transformer
self attention via prefix tokens learned through backpropagation on the task
data while keeping the pretrained model’s parameters frozen. Prompt tuning
based approaches have been shown to do well on lifelong learning (Qin and
Joty, 2021) and zero-shot domain adaptation (Zhao et al., 2022), which
inspires us to adapt it to domain generalization for abstractive
summarization. Concurrently, weight averaging has performed well on domain
generalization tasks in Computer Vision (Cha et al., 2021; Ramé et al., 2022;
Arpit et al., 2021). To improve functional diversity, these methods average
model parameters from different runs and/or checkpoints.

Matena and Raffel (2021) applied weight averaging to NLP tasks. They merged
models trained on different tasks/domains through fisher weight averaging.
Their promising results motivate us to apply model merging through weight
averaging for domain generalization. Keeping in mind the goal of generating a
lightweight and parameter-efficient approach, and the benefits brought by
weight averaging, we propose a lightweight Domain Aligned Prefix Averaging,
DAPA, approach to domain generalization for abstractive summarization. Our
algorithm consists of three stages. First, prefixes are trained for each
source domain. In the second stage, these source prefixes generate summaries
for a small number of unlabelled target domain documents. In the third stage,
the target domain prefix is obtained through a weighted average of these
source prefixes. A higher document-summary similarity score, calculated from
the summaries generated in the second stage, would assign a greater weightage
to the corresponding source prefix. Through our prefix averaging scheme, we
can identify source prefixes essential to ensure good performance on the
target domain. Our extensive experimentation on four domains demonstrates the
benefits bought by DAPA.

DAPA comes with the following advantages: i) It is a lightweight approach to
domain generalization since the backbone pretrained model is frozen and only
source prefixes are trained. ii) Through our novel prefix averaging scheme,
DAPA is able to generalize well onto target domains. Moreover, freezing the
backbone model’s parameters further preserves generalization. iii) Our
approach supports the efficient addition of new source domains since it only
involves recomputing the prefix averaging weights.

To this end, we summarize our contributions as follows:

  * •

To the best of our knowledge, we are the first to explore prefix averaging for
domain generalization on a language generation task.

  * •

We propose a lightweight Domain Aligned Prefix Averaging, DAPA, approach to
domain generalization for abstractive summarization. DAPA first trains
prefixes for each source domain, following which it utilizes the summary
generation capabilities of these source prefixes to generalize to the target
domain.

  * •

Through our experimentation setup we demonstrate the effectiveness of DAPA on
domain generalization for abstractive summarization.

The rest of the paper is structured as follows: We explore related works in
Section 2. Section 3 describes our proposed approach DAPA. Section 4 and 5
provide results for our domain generation experiments and a set of analysis we
conduct on our approach. Section 6 provides the conclusion and thoughts for
future work. Section 7 discusses limitations and Section 8 sheds light om
ethical risks of our work.

##  2 Related Work

###  2.1 Abstractive Summarization

Abstractive document summarization aims to distill the most critical
information in a document to present a concise view of it. Nallapati et al.
(2016) used an RNN based sequence to sequence model for abstractive
summarization. See et al. (2017) used pointer generator networks to copy words
from the input document. Duan et al. (2019) augmented the transformer
architecture with a contrastive attention mechanism to ignore the irrelevant
parts of the document. Zhang et al. (2020) pretrained a transformer model for
summarization. Liu and Liu (2021) used contrastive loss for better re-ranking
of summaries generated by pretrained models. Paulus et al. (2018) proposed the
use of policy gradient reinforcement learning to alleviate exposure bias.
Gehrmann et al. (2018) developed a bottom-up copy attention mechanism to over-
determine phrases in the document that should be included in the summary.
Although great progress has been made in advancing state-of-the-art, few works
have explored domain generalization for abstractive summarization. In this
work, we develop a lightweight prefix averaging based method for domain
generalization in abstractive summarization.

Figure 1: Overview of the Domain Aligned Prefix Averaging model. Source
prefixes are used for generating summaries for $m$ target domain documents.
The similarity of these summaries to their corresponding documents are used
for computing weights required to average source prefixes.

###  2.2 Prompt Learning in Language Generation

Prompts are task specific instructions prepended to the pretrained model’s
input. These task specific instructions are trained on the downstream task
data while keeping the pretrained model’s parameters frozen. Li and Liang
(2021) proposed deep continuous prefixes that are prepended to the self
attention layers of transformers. They demonstrated the effectiveness of using
prefixes for language generation tasks such as summarization. Qin and Joty
(2021) prepended prompts into model embeddings for lifelong learning on
language generation tasks. Similar to our approach, they train a separate
prompt for each domain, however, their work does not focus on domain
generalization. Tan et al. (2021) developed a multistage prompting network for
machine translation where the encoder is prompted twice to refine the input
representation, and the decoder is prompted once to generate the translation.
Schick and Schütze (2020) used manually crafted templates for fixed-prompt
tuning of pretrained models for few-shot summarization. Zhao et al. (2021);
Dou et al. (2021) used learnable prompts as guiding instructions for
summarization. Zhao et al. (2022), similar to our approach, used prefixes to
adapt to target domain. However, their approach involves pretraining prefix
weights and cannot easily incorporate new source domains. On the other hand,
our work takes a weighted average of source prefixes and can easily add new
source domains to the mix.

###  2.3 Weight Averaging and Domain Generalization

Domain generalization accounts for learning a robust model for unseen domains
from a set of source domains. It has mostly been studied in Computer Vision.
The main approaches are based on invariant feature learning (Li et al., 2018;
Wang, 2020), data augmentation (Wang et al., 2020a), and meta learning (Wang
et al., 2020b). For language generation, Vu et al. (2022) proposed a leave-
one-domain-out strategy to fuse adaptors for machine translation. Recently,
weight averaging (Garipov et al., 2018) has been successfully applied to
domain generalization. Cha et al. (2021) utilized weight averaging to attain a
flat minima, and thereby attained generalization across domains. Ramé et al.
(2022) averaged weights across multiple runs, trained with different
hyperparameters to improve domain generalization. Arpit et al. (2021)
ensembled a moving average of weights across multiple checkpoints. Unlike
these approaches, we develop a novel mechanism to generate weights for
averaging source prefixes, and evaluate our approach on abstractive
summarization. Matena and Raffel (2021) also developed a scheme to average
model weights. They utilized Fisher information in model parameters for weight
averaging. However, they only evaluated on NLU tasks.

##  3 Method

We first describe the domain generalization problem in Section 3.1. Then we
move on to describe prefix tuning in Section 3.2. Finally, in Section 3.3, we
describe our proposed approach, DAPA.

###  3.1 Problem Definition

Let $D^{S}=\\{D_{1}^{S},D_{2}^{S},...,D_{n}^{S}\\}$ be the set of source
domains. We denote the target domain with $D^{T}$. Domain generalization aims
to seek a network which generalizes well on $D^{T}$ when trained on $D^{S}$.
We require our model to generate fluent summaries for target domain documents
when trained on source domain documents.

###  3.2 Prefix Tuning

We utilize prefix tuning to train a separate prefix for each source domain. We
begin by restating the transformer attention:

$\text{attn}(Q,K,V)=\text{softmax}(\frac{Q\cdot K^{\top}}{\sqrt{d}})V$ (1)

Here, the query matrix $Q$, the key matrix $K$, and the value matrix $V$ are
obtained through independent linear transformations on the output of the
previous layer/encoder. $d$ is the model dimension. Note that we omit the
multihead notation for clarity.

Prefix tuning modifies the transformer attention by adding tunable prefixes to
$K$ and $V$. Consequently $K$ is modified as $K^{\prime}=[h_{K};K]$ and $V$ is
modified as $V^{\prime}=[h_{V};V]$. Here $h_{K}$ and $h_{V}$ represent the key
prefix and the value prefix respectively.

Following Li and Liang (2021), we model these prefixes using a two layer MLP
as follows:

$\displaystyle
h^{j}_{K}=W_{K,2}^{j}f(W_{K,1}^{j}E^{j}+b_{K,1}^{j})+b_{K,2}^{j}$ (2)
$\displaystyle
h^{j}_{V}=W_{V,2}^{j}f(W_{V,1}^{j}E^{j}+b_{V,1}^{j})+b_{V,2}^{j}$

where $W_{K,1}^{j},W_{V,1}^{j},W_{K,2}^{j},W_{V,2}^{j}\in\mathbb{R}^{d\times
d}$ are trainable weights, and
$b_{K,1}^{j},b_{V,1}^{j},b_{K,2}^{j},b_{V,2}^{j}\in\mathbb{R}^{d}$ are
trainable biases. $E^{j}\in\mathbb{R}^{C\times d}$ is a trainable embedding
matrix with $C$ as the prefix length. Index $j$ corresponds to source domain
$D_{j}^{S}$. We detail the initialization of $E^{j}$ in Section 4.3. Each
source prefix is trained in an end-to-end fashion on its corresponding source
domain data.

###  3.3 Domain Aligned Prefix Averaging

Having formulated our problem and described prefix tuning, we now describe our
approach, DAPA.

####  3.3.1 Computing Weights to Average Source Prefixes

DAPA utilizes the summary generation capabilities of source prefixes to
generate weights for averaging source prefixes. Let
$D^{T}_{m,sample}=\\{x_{1},x_{2},...,x_{m}\\}$ be a set $m$ unlabelled
documents from the target domain. In our experiments, we observe that a value
of $m$ as small as $50$ suffices. Let
$P^{S}=\\{P_{1}^{S},P_{2}^{S},...,P_{n}^{S}\\}$ represent the set of source
prefixes. Note that $P^{S}_{j}=\\{h_{K}^{j},h_{V}^{j}\\}$. For target domain
document $x_{i}$, DAPA first generates $n$ summaries pertaining to each source
prefix as follows:

$y_{i}^{j}=M(x_{i};P_{j}^{S})\text{; }\text{ }1\leq i\leq m\text{, }\text{
}1\leq j\leq n$ (3)

where $M$ represents the frozen pretrained language model.

Next, it uses an encoder $f$ to generate sentence representations for the
summary $y_{i}^{j}$ as $r_{i}^{j}=f(y_{i}^{j})$ and the document $x_{i}$ as
$t_{i}=f(x_{i})$. We use SentenceBERT (Reimers and Gurevych, 2019) as our
encoder. Following this, we compute average document-summary cosine similarity
scores for each source prefix as follows:

$s^{j}=\sum_{i=1}^{m}\text{cosine-similarity}(r_{i}^{j},t_{i})$ (4)

The final weights for averaging source prefixes are generated by taking a
softmax over the average document-summary similarity scores as:

$w^{j}=\frac{\text{exp}(s^{j})}{\sum_{i=1}^{n}\text{exp}(s^{i})}$ (5)

####  3.3.2 Prefix Averaging

Given a target domain document $x$, we wish to generate a summary $y$ with a
target prefix obtained by averaging the source prefixes using our weight
averaging scheme described in Section 3.3.1. Through
$W=\\{w^{1},w^{2},...,w^{n}\\}$, we take a weighted average of the source
prefixes as follows:

$\displaystyle h^{T}_{K}=\sum_{j=1}^{n}w^{j}h_{K}^{j}$ (6) $\displaystyle
h^{T}_{V}=\sum_{j=1}^{n}w^{j}h_{V}^{j}$ $\displaystyle
P^{T}=\\{h_{K}^{T},h_{V}^{T}\\}$

where $P^{T}$ is the target prefix through which the target summary
$y=M(x;P^{T})$ is generated. Note that test time averaging requires
recomputation of $h_{K}^{j}$ and $h_{V}^{j}$ by replacing $E^{j}$ with $E^{T}$
in equation 2. We detail the computation of $E^{T}$ in Section 4.3.

##  4 Experimental Setup

###  4.1 Dataset and Metrics

We use four summarization datasets, each belonging to a different domain. For
the news domain we use the CNN/Daily Mail dataset (Hermann et al., 2015);
Samsum (Gliwa et al., 2019) for the chat domain; Reddit posts for the social-
media domain (Kim et al., 2018); ScisummNet (Yasunaga et al., 2019) for
training on the scientific domain and Cl-SciSumm (Jaidka et al., 2018) for
testing on the scientific domain. Dataset statistics are presented in Table 1.
For evaluation, we report ROUGE-1, ROUGE-2 and ROUGE-L
metrics222https://github.com/pltrdy/files2rouge (Lin, 2004).

Domain | Train size | Dev size | Test size
---|---|---|---
News | 287,113 | 13,368 | 11,490
Scientific | 947 | 10 | 10
Social-media | 33246 | 4151 | 4155
Chat | 14,732 | 818 | 819

Table 1: Dataset statistics for each domain.

Approach | News | Scientific | Chat | Social-media
---|---|---|---|---
R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L
ERM-finetune | 39.94 | 18.09 | 32.90 | 33.72 | 22.14 | 31.66 | 25.52 | 6.69 | 21.09 | 16.53 | 2.94 | 11.58
ERM-prefix | 38.05 | 16.49 | 32.15 | 29.29 | 17.55 | 26.78 | 22.46 | 5.47 | 19.39 | 17.5 | 3.01 | 12.36
DAPA-average | 35.98 | 15.90 | 30.58 | 28.78 | 17.04 | 26.52 | 23.04 | 5.55 | 19.68 | 16.86 | 2.88 | 11.88
DAPA-max | 35.18 | 15.39 | 30.36 | 28.57 | 15.84 | 25.03 | 26.64 | 7.32 | 22.19 | 16.97 | 2.76 | 11.83
DAPA-inst | 35.93 | 15.91 | 30.58 | 29.34 | 18.72 | 27.40 | 20.81 | 4.78 | 17.79 | 16.54 | 2.81 | 11.64
DAPA | 40.28 | 18.12 | 32.78 | 30.84 | 18.97 | 27.23 | 28.23 | 8.70 | 22.86 | 14.48 | 2.68 | 9.70

Table 2: ROUGE scores for domain generalization on the four domains.

Target | Excluded | R-1 | R-2 | R-L
---|---|---|---|---
Domain | Domain
Scientific | News | 30.61 | 18.01 | 26.46
Chat | 30.20 | 19.54 | 26.78
Social-media | 31.25 | 19.26 | 27.26
Chat | News | 28.23 | 8.67 | 22.86
Social-media | 28.23 | 8.67 | 22.86
Scientific | 25.63 | 6.51 | 21.16
Social-media | News | 14.6 | 2.71 | 9.79
Chat | 14.6 | 2.71 | 9.79
Scientific | 17.25 | 3.04 | 12.24
News | Social-media | 40.29 | 18.12 | 32.78
Chat | 40.27 | 18.12 | 32.78
Scientific | 38.06 | 16.71 | 32.28

Table 3: ROUGE scores while using only two source domains to compute $W$. The
second column mentions the domain whose prefix has been left out for the
target domain’s prefix computation.

Target | Source | R-1 | R-2 | R-L
---|---|---|---|---
Domain | Domain
Scientific | News | 36.73 | 25.01 | 34.07
Chat | 32.14 | 19.08 | 28.15
Social-media | 18.81 | 9.42 | 17.40
Chat | News | 25.61 | 6.49 | 21.11
Social-media | 21.21 | 5.76 | 18.87
Scientific | 28.23 | 8.70 | 22.86
Social-media | News | 17.22 | 3.01 | 12.23
Chat | 16.37 | 2.72 | 11.61
Scientific | 14.61 | 2.71 | 9.79
News | Social-media | 30.47 | 13.49 | 27.14
Chat | 38.03 | 16.69 | 32.25
Scientific | 40.27 | 18.12 | 32.78

Table 4: ROUGE scores while using a single source domain’s prefix to generate
target domain summaries. The second column mentions the source domain adopted
to generate summaries on the target domain.

###  4.2 Baselines

We use the method of empirical risk minimization (ERM) as our primary
baseline. It trains the model by minimizing the sum of errors across source
domains and examples. For computer vision tasks, Gulrajani and Lopez-Paz
(2021) have shown that a well tuned ERM baseline performs competitively with
several sophisticated methods for domain generalization. Thus, we use it as
our primary baseline. We define two variants of ERM: i) ERM-finetune,
finetunes the pretrained language model on a combination of all source
domains. ii) ERM-prefix, prefix-tunes the backbone language model on a
combination of all source domains. To validate the efficacy of our weight
averaging scheme, we create two variants of DAPA, namely DAPA-average and
DAPA-max. DAPA-average follows $w^{j}=\frac{1}{n}$ and DAPA-max takes a max
pooling operation over the source prefixes instead of averaging them. We also
present results for an instantaneous version of DAPA, DAPA-inst. Here, instead
of using $D^{T}_{m,sample}$, we use the current testing document to compute
weights $W$. We also consider four additional baselines as an upper bound to
our method, results for which are presented in Appendix A.

((a)) ROUGE-1 variation.

((b)) ROUGE-2 variation.

((c)) ROUGE-L variation.

Figure 2: Variation of ROUGE scores with prefix length $C$. Empirically,
$C=50$ is the most optimal prefix length. Throughout this experiment, $m=50$.

###  4.3 Training Details

For our backbone pretrained model, we use T5-small (containing roughly 60M
parameters) (Raffel et al., 2020). Prefix tuning adds rough 922K test time
parameters to T5-small. Prefix length $C$ is fixed to $50$ unless otherwise
specified. $m$ is also set to $50$. We verify our choices for $C$ and $m$ in
Section 5. Both, finetuning and prefix tuning experiments are optimized with
Adafactor (Shazeer and Stern, 2018). Finetuning uses a maximum learning rate
of $5e-4$, a square root decay schedule, and a linear warmup of $5000$ steps.
Prefix tuning uses a constant learning rate of $5e-3$. All other Adafactor
specific hyperparameters are left to their default values in HuggingFace-
transformers333https://github.com/huggingface/transformers (Wolf et al.,
2020). We utilize OpenPrompt444https://github.com/thunlp/OpenPrompt Ding et
al. (2022) and HuggingFace-transformers to implement prefix tuning, and use
the sentence-transformers555https://huggingface.co/sentence-transformers/all-
MiniLM-L6-v2 implementation for SentenceBERT.

For finetuning, we employ a batch size of $5$ with gradient accumulation up to
$5$ iterations. For prefix tuning, we use a batch size of $5$ but without any
gradient accumulation. All our experiments are run on a single Nvidia-RTX 2080
Ti machine. One finetuning weight update (via gradient accumulation) takes
rough 224 milliseconds and one prefix tuning iteration takes roughly 139
milliseconds. All our models are trained for 10 epochs with early stopping
performed through validation ROUGE scores. For ERM-finetune and ERM-prefix,
the training process is stopped if the in-domain validation scores for any of
the three source domains starts to fall. Each training experiment is carried
out only once.

((a)) ROUGE-1 variation.

((b)) ROUGE-2 variation.

((c)) ROUGE-L variation.

Figure 3: Variation of ROUGE scores with the number of sentences used for
computing weights $W$. Throughout this experiment, $C=50$ and $E^{T}$ is
initialized with $50$ most frequent tokens from $D^{T}_{m,sample}$.

((a)) ROUGE-1 variation.

((b)) ROUGE-2 variation.

((c)) ROUGE-L variation.

Figure 4: Variation of ROUGE scores with the number of sentences used for
deriving the $C$ most frequent words. Throughout this experiment, $C=50$.

For prefix tuning, we initialize $E^{T}$ with T5 embeddings of the $C$ most
frequent sentencepiece666https://github.com/google/sentencepiece (Kudo and
Richardson, 2018) tokens of $D^{T}_{m,sample}$. For DAPA-inst, the same
process is applied, however instead of $D^{T}_{m,sample}$, the current test
document is used. For source domains, $C$ most frequent tokens are extracted
from the train set.

Summary generation uses a beam length of $10$ and a repetition penalty of
$2.5$. All source documents are truncated to $512$ sentencepiece tokens and
all summaries are truncated to $200$ sentencepiece tokens. For the scientific
domain, we only include the document’s abstract, introduction and conclusion
in our input to present the document’s most crucial aspects within T5’s
maximum allowed sequence length. All our results are presented on the test
sets of the four domains.

###  4.4 Main Results

Table 2 presents results for our domain generalization experiments. DAPA
outperforms all compared methods on the chat domain and outperforms all
compared methods on two out of the three ROUGE scores on the news domain.
Owing to our prefix averaging method, DAPA demonstrates better generalization
capabilities when compared to ERM-finetune and ERM-prefix. DAPA-max and DAPA-
average do not utilize the summary generation capabilities of source prefixes
and, thus, fail to account for the aspects most crucial to summary generation
for the two domains. Also, DAPA-inst performs significantly worse than DAPA,
thereby emphasizing the importance of using a greater number of target domain
documents to better approximate the weights for averaging source prefixes.

((a)) Weights obtained by DAPA for averaging source prefixes.

((b)) Probabilities assigned to the target domain by a BERT model trained for
source domain identification.

((c)) Probabilities assigned to the target domain by DAPA-alt.

Figure 5: Source domain preferences for the target domain obtained through
DAPA, a BERT model trained for source domain identification and DAPA-alt.

On the contrary, ERM-finetune outperforms DAPA on the scientific domain.
Yasunaga et al. (2019) demonstrate the superior performance of extractive
approaches over abstractive summarization approaches for the scientific
domain. Thus, the ability to copy phrases from the input document becomes
imperative to a good performance. Possibly, since all model parameters are
tuned for ERM-finetune, its ability to copy phrases from the input document
exceeds that of DAPA which only tunes the source prefixes. Despite this, DAPA
outperforms the other compared methods for reasons similar to the ones stated
previously.

Also, all compared methods outperform DAPA on the social-media domain. We find
that DAPA allocates a significant amount of weight to the scientific domain
prefix. However, we observe that the scientific domain adversely affects
performance on the social-media domain (Refer to Section 5.1 for more
details). This irregularity may have resulted from the encoder $f$. Further
investigation to this is left for future work. Only $27.94\%$ of the maximal
weights selected by DAPA-max belong to the scientific domain prefix. Also,
DAPA-average assigns equal weights to all the three source domains which is
less than the weight assigned by DAPA to the scientific domain. Thus both
DAPA-average and DAPA-max outperform DAPA. The noise added by DAPA-inst to the
weight calculation process results in a smaller weight assigned to the
scientific domain prefix (0.35 vs 1.00) as a result of which DAPA-inst
outperforms DAPA. Owing to the larger dataset size for the chat and news
domains, ERM-prefix and ERM-finetune are less impacted by adverse effects of
the scientific domain and thus outperform DAPA. ERM-finetune underperforms
ERM-prefix probably because of its larger capacity to retain scientific domain
knowledge.

##  5 Analysis

To study the impact of various factors in DAPA, and better understand its
efficacy, we conduct a series of analysis.

###  5.1 Effect of Source Domains on the Target Domain

In Table 4, and Table 4, we analyze the effect of various source domains on
the target domain. Throughout these experiments, $m=50$ and $C=50$. Note that
the experiments in this section only require recomputation of the prefix
averaging weights and thus support the claim that DAPA allows for
computationally efficient addition of new source domains. For the scientific
domain, we can see that the performance is best when only using the news
domain, in fact, it outperforms ERM-finetune from Table 2. Adding the chat and
social-media domains only hampers performance. The performance is worst when
using only the social-media domain. The improvement over this result indicates
that DAPA is able to assign appropriate weights to the three source domains
allowing for a greater contribution from the news and chat domains. In real-
world applications where the number of domains is significantly greater than
our setting, and there is no labelled data to measure the performance over the
target domain (to decide the optimal set source prefixes to be averaged), DAPA
offers an effective scheme to choose the most appropriate source prefixes.

In the case of the chat domain, ablating the scientific domain results in
degraded performance. On the contrary, excluding the other two prefixes does
not affect DAPA’s performance. Similarly for the news domain, removing the
scientific domain’s prefix results in a significant drop in all three ROUGE
scores and removing the chat domain results in a slight drop in ROUGE-1 score.
Here, we see that both the chat, and the scientific domain contribute to the
performance since using only one of them underperforms the result in Table 2.

In the case of the social-media domain, excluding the scientific domain
significantly improves DAPA’s performance. Also, both the news and the chat
domain contribute to the performance since using only one of them
underperforms their weighted average.

###  5.2 Effect of Prefix Length $C$

An analysis on the effect of $C$ is presented in Figure 2. $C$ most frequent
words in the target domain are extracted from $m=50$ documents. In general, we
observe the target domain performance increases up to $C=50$ following which
it either drops or remains more or less the same. Thus, we select $C=50$ for
DAPA. $scientific$ domain ROUGE scores drop significantly after $50$ prefix
tokens. We leave further exploration into this for future work.

Approach | News | Scientific | Chat | Social-media
---|---|---|---|---
R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L
DAPA-embed | 39.06 | 17.96 | 31.81 | 28.92 | 16.79 | 26.24 | 26.32 | 8.21 | 21.31 | 13.04 | 2.76 | 8.80
DAPA | 40.28 | 18.12 | 32.78 | 30.84 | 18.97 | 27.23 | 28.23 | 8.70 | 22.86 | 14.48 | 2.68 | 9.70

Table 5: ROUGE scores for initializing $E^{T}$ as a weighted average of
$E^{j}$s, i.e. DAPA-embed.

Approach | News | Scientific | Chat | Social-media
---|---|---|---|---
R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L
DAPA-alt | 36.12 | 15.96 | 30.70 | 29.42 | 18.02 | 27.32 | 23.57 | 5.84 | 20.05 | 16.81 | 2.90 | 11.82
DAPA | 40.28 | 18.12 | 32.78 | 30.84 | 18.97 | 27.23 | 28.23 | 8.70 | 22.86 | 14.48 | 2.68 | 9.70

Table 6: ROUGE scores for an alternative way of computing $w^{j}$s as
discussed in Section 5.5, i.e. DAPA-alt.

###  5.3 Effect of $m$ on $W$

An analysis on the effect of the number of sentences used for computing
weights to average source prefixes is presented in Figure 3. Beyond $m=20$,
the performance on the target domain remains more or less constant. Thus, we
stick to our initial choice of using $50$ sentences to compute weights $W$.

An analysis on the effect of the number of sentences used for obtaining $C$
most frequent tokens is presented in Figure 4. Again, the performance does not
vary significantly beyond $m=20$. Thus, we hold to our initial choice of
$m=50$ for our main experiments. Note that we do not include results on the
scientific domain for this subsection since its test set has only $10$
instances, and we use all of them for our experiments.

###  5.4 Does Averaging over Source $E^{j}$s Help?

In our main method, we initialize $E^{T}$ with $C$ most frequent sentencepiece
tokens from the $m$ unlabelled documents. Here, we explore an alternative way
of initializing $E^{T}$ wherein we use $w^{j}$s to take a weighted average of
source $E^{j}$s :

$E^{T}=\sum_{j=1}^{n}w^{j}E^{j}$ (7)

Results for this initialization scheme (DAPA-embed) are presented in Table 5.
DAPA outperforms DAPA-embed across domains demonstrating the benefits of
supplying $P^{T}$ with some prior target domain knowledge by initializing
$E^{T}$ with $C$ most frequent sentencepiece tokens from the $m$ unlabelled
documents.

###  5.5 Does Softmax before Summation Help?

Here, we propose an alternative way of computing $w^{j}$. Instead of summing
over the document-summary cosine similarities (Equation 4) and then applying
the softmax operation (Equation 5), we first apply the softmax operation to
document-summary similarity scores following which we average over them. That
is, we replace Equation 4 with:

$w_{i}^{j}=\frac{\text{exp}(\text{cosine-
similarity}(r_{i}^{j},t_{i}))}{\sum_{k=1}^{n}\text{exp}(\text{cosine-
similarity}(r_{i}^{k},t_{i}))}$ (8)

and Equation 5 with

$w^{j}=\frac{1}{m}\sum_{i=1}^{m}w_{i}^{j}$ (9)

By doing so, we are flattening the weights $w^{j}$. This is evident from
Figure 5(c) where the target domain assigns near equal weights to all three
source domains. This is different from DAPA, where the weights $w^{j}$ are
sharp as depicted in Figure 5(a). In Table 6, DAPA outperforms this
alternative strategy (DAPA-alt) on three out of the four domains. A flattened
$w^{j}$ distribution approaches DAPA-average, and thus, does not benefit from
DAPA’s weight averaging scheme. DAPA-alt outperforms DAPA on the social-media
domain since it assigns near equal weights to each source domain (Refer to
Section 4.4 for a detailed analysis).

###  5.6 Is DAPA correlated to Document Similarity?

We analyze how DAPA’s weight assigning process aligns with source-target
domain similarity at the document level. For this we train BERT-base (Devlin
et al., 2019) on 300 source domain documents (100 from each source domain) for
source domain identification. We evaluate this model on the target domain’s
$D^{T}_{50,sample}$. We plot the average probabilities assigned to each source
domain in Figure 5(b). Also, in Figure 5(a), we plot weights $W$ computed by
DAPA. Note that BERT-base achieves perfect test accuracy when evaluated on an
in-domain validation set for each training setting. Entries of the form
$[x,x]$ are always zero owing to our domain generalization setting.

From the two plots, it is clear that DAPA’s weight assignment process does not
always correlate with source-target domain similarity at the document level.
For the news domain, as per Table 4 and Table 4, the scientific domain
contributes most to the model’s performance, on the other hand, BERT assigns
near equal probability to the social-media and chat domain, and assigns near
zero probability to the scientific domain. Similarly, for the chat domain, the
scientific domain is vital to good performance, however, BERT assigns a low
probability to it. Whereas, for the social-media and scientific domain, BERT
does a better job and assigns a higher probability to the news domain. These
results navigate us to the conclusion that DAPA does not use document level
similarities and indeed relies on the summary generation capabilities of
source prefixes.

##  6 Conclusion

In this paper, we present DAPA, a lightweight, domain aligned prefix averaging
approach to domain generalization in abstractive summarization. DAPA utilizes
source prefixes to generate summaries for a small number of target domain
documents. The similarity of these summaries to their corresponding documents
are used for calculating weights required to average source prefixes. DAPA can
easily account for the addition of new source domains since only the prefix
averaging weights need to be recomputed. On four diverse summarization
domains, DAPA either performs comparably or outperforms the baselines. We also
perform an in-depth analysis of various components of DAPA to further
strengthen our design choices. In future, we would like to develop an improved
similarity function $f$ and analyze the loss landscapes of these models to
corroborate our prefix averaging strategy.

##  7 Limitations

Our work focuses on domain generalization for abstractive summarization
through prefix averaging. However, we do not experiment with larger backbone
models due to computational constraints. Based on previous works we expect our
approach’s performance to improve with model size. Also, a larger sequence
length for prefix tuning increases the computational costs at inference.

Another limitation of our work is that we do not test it on natural language
understanding tasks. This can be part of a future work.

##  8 Ethical Statement

We consider our approach to have low ethical risks since we do not utilize any
data biases. Our approach could be extended to any natural language generation
task and does not constraint the input/output structure. We therefore conclude
that our method would not bring any harmful ethical impact.

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##  Appendix A Additional Results

Approach | News | Scientific | Chat | Social-media
---|---|---|---|---
R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L | R-1 | R-2 | R-L
Finetune-target | 37.65 | 17.05 | 32.06 | 39.66 | 21.76 | 35.57 | 32.65 | 12.43 | 29.03 | 17.38 | 2.92 | 12.77
Prefix-target | 38.10 | 17.61 | 33.11 | 49.88 | 30.64 | 43.19 | 39.30 | 15.67 | 33.49 | 19.48 | 4.10 | 15.35
Finetune | 41.20 | 19.18 | 35.20 | 52.97 | 32.27 | 45.85 | 49.42 | 24.31 | 42.43 | 25.06 | 7.23 | 19.96
Prefix | 40.56 | 19.07 | 34.94 | 49.58 | 29.72 | 42.84 | 46.20 | 22.48 | 40.27 | 23.22 | 6.30 | 18.59
DAPA | 40.28 | 18.12 | 32.78 | 30.84 | 18.97 | 27.23 | 28.23 | 8.70 | 22.86 | 14.48 | 2.68 | 9.70

Table 7: Results for the four additional baselines.

For the sake of completeness, we present four additional baselines in Table 7.
Finetune-target assumes that the $m$ target domain documents are labelled and
thus uses standard finetuning to update T5-small parameters with these $m$
documents. Likewise, Prefix-target uses prefix tuning to train a prefix from
scratch with these $m$ documents. Since, the above two baselines use labelled
documents they cannot be directly compared with DAPA and only present an upper
bound to the performance of DAPA. Using $m$ labelled documents reaps benefits
on three out of the four domains. Note that we use $m=50$ for the above two
baselines.

We also present results for full finetuning (Finetune) and full prefix tuning
(Prefix) on each of the four domains. Finetune uses the target domain’s entire
training set to update T5-small parameters and Prefix trains a prefix from
scratch on the target domain’s training set. Again, these two baselines only
act us upper bounds to DAPA and outperform it across domains.


# When Physical Unclonable Function Meets Biometrics

Kavya Dayananda
Department of Computer Engineering
San Jose State University
San Jose, CA 95119
<EMAIL_ADDRESS>
Nima Karimian
Department of Computer Engineering
San Jose State University
San Jose, CA 95119
<EMAIL_ADDRESS>

###### Abstract

As the Covid-19 pandemic grips the world, healthcare systems are being
reshaped, where the e-health concepts become more likely to be accepted.
Wearable devices often carry sensitive information from users which are
exposed to security and privacy risks. Moreover, users have always had the
concern of being counterfeited between the fabrication process and vendors’
storage. Hence, not only securing personal data is becoming a crucial
obligation, but also device verification is another challenge. To address
biometrics authentication and physically unclonable functions (PUFs) need to
be put in place to mitigate the security and privacy of the users. Among
biometrics modalities, Electrocardiogram (ECG) based biometric has become
popular as it can authenticate patients and monitor the patient’s vital signs.
However, researchers have recently started to study the vulnerabilities of the
ECG biometric systems and tried to address the issues of spoofing. Moreover,
most of the wearable is enabled with CPU and memories. Thus, volatile memory-
based (NVM) PUF can be easily placed in the device to avoid counterfeit.
However, many research challenged the unclonability characteristics of PUFs.
Thus, a careful study on these attacks should be sufficient to address the
need. In this paper, our aim is to provide a comprehensive study on the state-
of-the-art developments papers based on biometrics enabled hardware security.

_K_ eywords Biometrics, Key Genertion, PUFs, Hardware Security, Blockchain

##  1 Introduction

As the Covid-19 pandemic grips the world, Telehealth, and Telemedicine play
crucial roles to provide a better quality of care and patient monitoring as
they do not rely on in-person services. According to CDC [1], Telemedicine
minimizing the spread of SARS-CoV-2 while it provides necessary care to
patients. Although there are many benefits of healthcare application, there
has always been a threat of hackers getting access to the sensitive
information of the patients that can make misuse that information leading to
problems [2, 3]. Telemedicine and its intersection with wearable devices
accelerate patient insights. Therefore, the increased usage of biometric
technologies raises security and patient privacy risks, especially in
unprotected and hostile environments. In addition, cybercriminals around the
world work continuously to discover new techniques to invade the systems.
There is a need for patient authentication such that it can protect the user’s
privacy and their sensitive personal data. To address this issue, biometric
technology such as electrocardiogram (ECG) and photoplethysmogram (PPG) are
the best candidates [4]. Apart from its features for extracting health data
insights and diagnosis through healthcare monitoring [5, 6], ECG enables user
authentication based on physiological signals[7, 8, 9]. Moreover, consumer
electronics such as wearable devices have always had the concern of being
counterfeited between the fabrication process and vendors’ storage [10].
Hence, it is necessary for the hospital and user to verify the devices. To
address this concern, some of the existing components in the wearable devices
such as memory can make a security layer on top of the system gathering
signals from a patient. Physical Unclonable Functions (PUFs) are one of the
hardware solutions commonly used to authenticate with any given device [11,
12, 2]. In the continuation of this article, we will discuss current
challenges of biometric, PUFs, and future possibilities in combining two
advances in physical unclonable functions (PUFs) and biometrics systems.

##  2 Background on ECG Biometric

The work of Odinaka et al. [13] presents a unique short-time frequency method
for ECG biometric. A large dataset with a sample size of 269 was obtained from
the general population. These single lead ECG samples were collected over a
time period of seven months at different days and times. Also, care was taken
to choose subjects from different geographical regions and health statuses.
Additionally, the samples were collected in a manner that presented least
discomfort to the subjects by placing the electrodes bilaterally on the lower
ribcage. After collecting the data samples, filtering and preprocessing
techniques were applied on them to reduce the effect of noise on the input
sample. First the signals were digitally notch filtered to remove the
interference from the power line. After filtering, the R-wave peaks of the
signal are detected and the signal is segmented and aligned with respect to
the obtained R-wave peak. The other fiducial points $P,Q,S,T$ are neither
detected nor used in this method. Finally each of the ECG signals is
normalized. Normalization is done by subtracting the sample mean of the pulse,
and dividing by the sample standard deviation. Finally for selecting the
features, the authors propose a novel method for feature selection based on
the distinguishability and stability of features. From each ECG pulse signal,
a spectrogram value is calculated .It is computed by considering the logarithm
of the square of the magnitude of the short-time Fourier transform of a
normalized ECG heart pulse. This method achieved an accuracy of upto 93.5%.
Furthermore, the presented an 5.58% error and 76.9% accuracy during train-test
phases for heartbeats from different days. For data from a single day, the
train-test error rate achieved was 0.37% and 99% accuracy for decisions based
on a single heartbeat.

Li et al. [14] propose an alternative approach to perform analysis of the
signals in temporal and spatial domains. They extract ECG features from time
slices that make up the signal known as spectrum. The features extracted are
then used with the temporal information to derive a heterogenous model to
determine the heart rate and fiducial characteristics. They achieve up to 99%
on the MIT-NIH database. They show that identification of spatio-temporal
characteristics using gaussian mixture method (GMM) and support vector machine
based temporal analysis improve the accuracy to 98% from 89% that uses
traditional techniques. On the other hand , Labati et al. [9] use
convolutional neural networks to predict ECG signal characteristics. They use
ECG signals from the ECG E-HOL-03-0202-003 database to derive the test and
train data for the neural networks. They preprocess the ECG data from the
database and then use it for feature extraction using the neural network. The
preprocessing consists of filtering the noise and deriving a subset of the
input sample called feature vector that is used for all subsequent processing.
They have a 12-layer convolutional neural network to identify the features
from the feature vector for the given test and train samples. This method
achieved an accuracy of up to 97% and an error rate of 2.90%.

##  3 Background on Biometric Key Generation

There have been numerous proposals for generating keys from biometrics. The
key requirements for biometic key is to meet the requirement such as
randomness and uniqueness [15, 16]. Generally, biometric features will be
transformed into a binary string of various lengths to represent biometric
keys. Biometric keys generated should be uniformly random in order to remain
resistant to attack. There have been many research on biometric key generation
developed such fingerprint [17, 18], face [19], hand geometry [20], and iris
[21]. Not only the aforementioned biometric keys are vulnerable to spoofing
attacks, but also implementing biometric key based on iris, face is not
feasible in Telemedicine. Thus, ECG based biometric key generation has been
introduced. ECG based bio-key not only offers liveness and are difficult to
spoof, but also it is more feasible to be implemented in Telemedicine systems
as its extracting health data insights and diagnosis through healthcare
monitoring. Early works proposed interpulse intervals (IPIs) to use
electrocardiograms (ECGs) as an entropy source for generating random binary
sequences [22, 23, 24, 25, 23]. The IPI is defined as the time interval
between two successive R peaks. First steps is to clean the ECG signal from
noise sources. To eliminate the noise, the ECG signal is passed through a
Butterworth pass-band filter. Second, using Pan–Tompkins algorithm, R peak
will be identified. Second, the time difference between two consecutive
R-peaks are computed to generate IPIs. Each IPI will be converted into values
between 0 and 1. The 128 key bits will be generated based on LSB of each IPIs.
The drawback of this technique is that it requires a long period of IPIs [26,
22]. The example of IPI key generation is demonstrated in the Fig 1. As can be
seen in this figure, due to low entropy of the most-significant bits (MSB),
only least significant bit (LSB) are considered and MSB bits are discarded.

While biometrics have their advantages, keys generated from biometrics may
suffer from environment noise. These limitations could introduce binary errors
during key generation and make it impossible for even the system owner to
access/use the system. Thus, IPI techniques heavily rely on error correction.
In order to overcome these detriments, a statistical approach to mitigate or
eliminate the intra-subject variance while preserving privacy and generating
long keys have been developed [16, 27]. The statistical approach begins with
an initialization phase. ECG Features are extracted from ECG signals from a
large population of users. Statistics for each feature are used to determine
thresholds and boundaries to quantize the feature into one or more key bits.
The number of bits depends on the parameters and statistics, and can vary from
feature to feature. Compared to IPI methods, statistics approach use single
heartbeat to generate 128 key bits. The proposed system containing biometric
pre-processing, feature extraction, etc. and reconfigurable logic to act as
the permutation block. The reconfigurable logic forms is programmed according
to the operators biometric. They used permutation chip to insert a binary key
bits and lock the system. If the correct biometric which belongs to the
authorized owner is asserted, the system will unlock allowing the inputs pass
to the correct outputs of the permutation chip. Otherwise, the system will not
work.

Figure 1: ECG Key generation using the interpulse intervals (IPIs) [28].
Figure 2: ECG Key generation using the statistical approach [16].

##  4 Background on PUFs

PUFs take advantage of the unique physical characteristics of an integrated
circuit (IC) that result from manufacturing and process variation in
nanoscales, giving each its own fingerprint. A PUF relies on inherent entropy
as well as stability/reliability to provide uniquely secure, yet consistent
operation based on a unique set of challenge (input) and response (output)
pairs. PUF’s uniqueness involves determining whether or not it provides a
different enough signature for its given IC to clearly differentiate it from
other ICs of the same kind. Computing the Hamming distance (HD) between a pair
of PUF identifiers (or the number of bit positions that differ in value) is
one way this can be done. Reliability refers to how well a PUF is able to
provide a consistent response to the same challenge. This is crucial since a
PUF should not be easily worn out or affected by environmental factors. There
are many forms of existing PUFs, including delay-based PUFs such as arbiter
PUFs [29, 30], ring-oscillator PUFs [31, 32, 33, 34]; memory-based PUFs such
as DRAM PUFs [35, 36, 37, 38] and SRAM PUFs [39, 40, 41], etc. “Weak and
Strong PUFs" are the two subtypes of PUFs. Weaks PUFs are used for storing
secret keys to non-volatile memories since they show some internal, unclonable
physical disorder, and they are involved in some form of challenge-response
mechanism which should be access-restricted. It is considered that even by
having the PUF-carrying hardware, the adversaries cannot access the Weak PUF’s
responses. Formerly, weak PUFs were toward special purpose circuits, now they
are based on intrinsic PUFs built from CMOS parts which are more affordable.
Strong PUFs have numerous CPRs which each time provide a new CPR for the
procedure of authentication. They exhibit a more complex challenge-response
pair. They are much harder to be predicted since if an adversary knows a large
subset of CRPs, it is almost impossible for them to predict the other unknown
CRPs. Strong PUFs have public access for challenge-response mechanisms that
allow everyone with physical possession of the PUF to apply challenges to the
Strong PUF which leads to some downsides such as the need for many CRPs to
remain secure [42].

Figure 3: PUFs Taxonomy [43].

##  5 Combining Hardware Security and Biometric Systems

Since unique signature of the wearable device using PUF do not vary for
different users and unique signature of a user using biokeys do not vary
across multiple devices, Cherupally et al. [44] combined ECG biokey, heart
rate variability (HRV), and SRAM-based physical unclonable function (PUF) to
perform real-time authentication and generate unique/random signatures. The
purpose of the proposed work was to enhance randomness. As can be seen in
Fig4, 256 key bits from individual’s ECG and HRV are combined with 256 key
bits of each device’s PUF using bitwise XOR to generate a 256-bit secret key.
Then this key will be used as one-time key for authentication. This methods
will reduce attack scenario to know both the PUF as well as the user’s ECG/HRV
features to uncover the root of trust [44].

Guo et al. [45] combined ECG biokey and physical obfuscation/locking to
protect electronic system by physical obfuscation/locking. The purpose of the
proposed work is avoid fault injection, and reverse engineering. They have
considered two approaches for users enrollment. In the first approach
authorized user could present his/her biometric to the system.Alternatively, a
database containing biometric keys from authorized user can be used to
configure the system. The second approach has the advantage that the user need
not be present during enrollment. To that, the system could be enrolled in a
secure location and then sent to the user. In that case, if the device is
captured by unauthorized user, it could not be used until it reaches its
enrolled user.

Figure 4: Combining hardware security with biometric system. Features based
ECG, HRV and SRAM PUF are extracted and combined to form a 256-bit secret key
[44].

Karimian et al. [46], combined ECG key bits, physical unclonable functions
(PUFs) and hardware obfuscation to provides non-invertibility and non-
linkability charchtestics of biometric systems. The purpose of using PUF is to
avoid permanently storing the human biometric and/or cryptographic keys on the
device. The PUF also provides a non-invertible transform to the biometric in
order to protect the biometric template and make the biometric/obfuscation
non-linkable to different devices. The hHardware obfuscation has been replace
in the biometric matching systems. The users ownership is taken by presenting
his/her biometric signal to the device. A preprocessing algorithm is applied
on the received biometric to extract the binary bio key. A PUF is used as a
one-way transform on the quantized biometric. In order to generate the PUF
challenge, the bio key is processed by a hardware hash function to the desired
length. An obfuscated bitstream is produced that will exploit this obfuscation
key to lock the system. During the authentication process, the user provides
his/her biometric as input. The same pre-processing algorithm as the
enrollment process is applied to generate the $bio-key$. Potential errors are
corrected with the helper data. Next, the hash function creates the challenge.
The $obs-key$ is then generated by injecting this challenge into the strong
PUF. Different from the enrollment process, this obfuscation key is generated
on the physical device instead of its mathematic model. A correct $obs-key$
unlocks the obfuscated bitstream and brings the device into functional
(unlocked) mode. Without the correct key, the device will simply not work
correctly. Arjona et al. [47] obfuscated the biometric data with PUFs based on
dual-factor authentication protocol to avoid storage of secret keys for IoT
application. They propose fingerprint recognition solution to identify the
user and that protects biometric data with the PUF response at the sensor
node.

###  5.1 ECG based Biometric and Blockchain

The advancement of microfabrication has led to every small device having its
own way of establishing network connectivity and access to the internet. This
ecosystem of interconnected devices is known as the Internet of Things (IoT).
IoT has paved the way for a wide range of applications in several fields.
Wearable health monitoring devices like smartwatches and Fitbit are one such
application of IoT. These portable devices use various sensors to measure
vital information of the human body like the pulse rate, heart rate, and other
useful personal health metrics. So with the easy access of ECG information
from these smart devices, there has been increasing interest in using ECG data
as biometrics in a variety of applications. Also, ECG signals, similar to
fingerprints, are unique to the individual and are very difficult to alter or
replicate by external means thereby making them a perfect candidate for
biometric especially for security applications. However, a major concern for
these applications is the need for transmitting these sensitive medical data
and ECG signals securely across the decentralized IoT ecosystem. Blockchain
technology provides a promising solution in this regard. Blockchain provides a
decentralized reliable and secure mechanism for transmitting the data.
Furthermore , blockchain is immutable , thus ensuring that there is no
tampering of the data. Blockchain has been historically used by crypto
currency coin base for distributed management of assets across the world in a
safe and secured manner. Such a system has been considered in reference to
medical data so that different stakeholders such as pharmacies, doctors and
health care providers can integrate to a comprehensive and unified health care
system. One such effort is provided by Vazirani et al. [48] towards effective
healthcare record management. They create a blockchain for pertinent data
ownership by associated stakeholders. This way they allow distributed
ownership of patient data without compromising on privacy. For example : the
lab testing units own the measured data , whereas the doctors will own the
medicine prescription and the pharmacist will monitor the prescription data to
pack the medicines for the patient. This way, they securely decentralize the
patient’s medical history and use the block chain as a distributed ledger to
make medical history as a unifying factor for the british healthcare system.
In their study , they have shown that such a distributed ledger can support
high throughput machine learning based analysis for better healthcare and also
provide secured personalised healthcare pathways.

##  6 Conclusions

The advancement of microfabrication has led to every small device having its
own way of establishing network connectivity and access to the internet. This
ecosystem of interconnected devices is known as the Internet of Things (IoT).
IoT has paved the way for a wide range of applications in several fields.
Wearable health monitoring devices like smartwatches and Fitbit are one such
application of IoT. These portable devices use various sensors to measure
vital information of the human body like the pulse rate, heart rate, and other
useful personal health metrics. So with the easy access of ECG information
from these smart devices, there has been increasing interest in using ECG data
as biometrics in a variety of applications. Also, ECG signals, similar to
fingerprints, are unique to the individual and are very difficult to alter or
replicate by external means thereby making them a perfect candidate for
biometric especially for security applications. However, a major concern for
these applications is the need for transmitting these sensitive medical data
and ECG signals securely across the decentralized IoT ecosystem. Blockchain
technology provides a promising solution in this regard. Blockchain provides a
decentralized reliable and secure mechanism for transmitting the data.
Furthermore , blockchain is immutable , thus ensuring that there is no
tampering of the data. The below section presents different techniques to
leverage ECG data for biometric and blockchain technology. Moreover, Wearable
devices often carry sensitive information from users which are exposed to
security and privacy risks. Moreover, users have always had the concern of
being counterfeited between the fabrication process and vendors’ storage.
Hence, not only securing personal data is becoming a crucial obligation, but
also device verification is another challenge. To address biometrics
authentication and physically unclonable functions (PUFs) need to be put in
place to mitigate the security and privacy of the users.

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