11institutetext: Polytechnic School PUCRS, Porto Alegre, Brazil
22institutetext: School of Computing Newcastle University, Newcastle upon
Tyne, UK

# Impact of using a privacy model on smart buildings data for CO2 prediction

Marlon P. da Silva 11 Henry C. Nunes 11 Charles V. Neu 22 Luana T. Thomas 11
Avelino F. Zorzo 11 Charles Morisset 22

###### Abstract

There is a constant trade-off between the utility of the data collected and
processed by the many systems forming the Internet of Things (IoT) revolution
and the privacy concerns of the users living in the spaces hosting these
sensors. Privacy models, such as the SITA (Spatial, Identity, Temporal, and
Activity) model, can help address this trade-off. In this paper, we focus on
the problem of $CO_{2}$ prediction, which is crucial for health monitoring but
can be used to monitor occupancy, which might reveal some private information.
We apply a number of transformations on a real dataset from a Smart Building
to simulate different SITA configurations on the collected data. We use the
transformed data with multiple Machine Learning (ML) techniques to analyse the
performance of the models to predict $CO_{2}$ levels. Our results show that,
for different algorithms, different SITA configurations do not make one
algorithm perform better or worse than others, compared to the baseline data;
also, in our experiments, the temporal dimension was particularly sensitive,
with scores decreasing up to $18.9\%$ between the original and the transformed
data. The results can be useful to show the effect of different levels of data
privacy on the data utility of IoT applications, and can also help to identify
which parameters are more relevant for those systems so that higher privacy
settings can be adopted while data utility is still preserved.

###### Keywords:

Privacy CO2 Prediction Smart Buildings Sensors Data

##  1 Introduction

The impact of the quality of an indoor environment on the well-being of its
occupants is a relatively well-studied problem [2]. More than 20 years ago,
Redlich et al. noted the increase of the Sick-Building Syndrome (SBS), which
includes “upper-respiratory irritative symptoms, headaches, fatigue, and
rash”[22]. Although they deemed $CO_{2}$ as “an unlikely cause of SBS”, a
study widely covered in the general press, clearly indicates potential health
risks associated with chronic exposure to environmentally relevant elevations
in ambient $CO_{2}$, including “inflammation, reductions in higher-level
cognitive abilities and impact on different body organs”[11]. There is,
therefore, a clear need for precise and reactive monitoring of indoor
$CO_{2}$, to detect and prevent dangerous situations.

On the one hand, smart buildings deploy IoT architecture usually including
$CO_{2}$ and temperature sensors [32], intended to be used on new services
that can be provided, many supported by Machine Learning (ML) techniques.
Those services are intended to automatise management and optimise user
comfort, security, and safety, quite often with a focus on occupancy
measurement [16, 17, 5]. The need for $CO_{2}$ monitoring is likely to push an
increasing deployment of such systems.

On the other hand, this monitoring faces an increasing privacy concern related
to ambient infrastructures. Naieni et al. for instance showed that although
roughly half of the participants in a survey were comfortable or very
comfortable with the collection of presence and temperature data, people
nevertheless favour data collection in which they cannot be identified
immediately and do not want inferences to be made from otherwise anonymous
data [21].

Lately, new legislation has been introduced to support and regulate personal
data usage and people’s privacy preferences, e.g. GDPR (General Data
Protection Regulation)111https://gdpr.eu/ in the EU/UK and LGPD (Lei Geral de
Proteção de Dados)222https://www.serpro.gov.br/lgpd/menu/a-lgpd/o-que-muda-
com-a-lgpd in Brazil. A common principle is that of data minimisation, which
specifies that a system should not collect and process more data than needed
for its purpose. There is also a clear concern that users must be involved in
data collection and processing and their preferences must be considered. As a
result, a data protection system must work out a difficult trade-off: minimise
data collection to satisfy as much as possible user privacy preference while
avoiding a loss to data utility, which might reduce the efficiency of
processing, and as a consequence could impact the overall utility of the data
to the provided services.

In this paper, we explore this trade-off in the context of a real-world smart
building by evaluating how different ML methods perform to predict $CO_{2}$
when different privacy levels are defined. We also evaluate how different
levels of privacy impact data utility in comparison to when the whole data is
available.

The remainder of this work is organised as follows. Section 2 presents
background on smart buildings, data privacy, and machine learning. Section 3
presents our methodology to configure the dataset and to build our
implementation. An experimental evaluation and discussion are presented in
Section 4. Section 5 discusses recent works on privacy in smart spaces and
$CO_{2}$ prediction using smart buildings sensor’s data. Finally, Section 6
concludes this work and indicates some future work directions.

##  2 Background

With the revolution of IoT equipment, many smart buildings are emerging,
especially in universities and business offices. They are responsible for
collecting a huge amount of data from many people every day. In light of this,
there is a growing concern about the privacy of these data.

For our research, we collected data from a smart building located at a
university in England, applied different settings of the SITA privacy model
that will be discussed in the next sections, and used machine learning
algorithms to check if there were changes regarding the usability of the data.

Thus, we investigated and collected some information on the topics covered,
which are organised as follows in the next sections: Section 2.1 deals with
smart buildings, Section 2.2 presents the SITA model, and Section 2.3
describes some machine learning algorithms applied in this work.

###  2.1 Smart buildings sensors and data

Smart building is a term that has its origins in the larger scenario of
building automation, which is the set of practices aimed to improve the
control of a building by electronic means. From building automation, emerges
the concept of intelligent building, when, in addition to control, we also
have historical data enabling us to make predictions[4].

The drivers for the development of buildings can be said to revolve around
adding value to a building[27]. Reducing energy consumption has now become a
driver in its own right, due to increasingly stringent regulations and
awareness of climate change. This is recognised in modern buildings as a
significant design criterion[9]. In order to achieve these requisites, there
are four specific approaches to follow:

  * •

the methods by which building operation information is gathered and responded
to (intelligence);

  * •

the interaction between the occupants and the building (control);

  * •

the building’s physical form (materials and construction); and

  * •

the methods by which building use information is collected and used to improve
occupant performance (enterprise).

Smart Buildings are Intelligent Buildings, but with additional, integrated
aspects of adaptable control, enterprise and materials, and construction. In
Smart Buildings, the four methods used to meet the drivers to building
progression, mentioned previously, are developed alongside each other,
utilising information from one in the operation of another. This is in
contrast to Intelligent Buildings, which have largely developed intelligence
independently of the other methods.

###  2.2 Privacy Model

In recent years there was an increase in demand for privacy techniques [25]
[24]. This comes in line with an increase in awareness of society to how
easily data is collected, distributed, and used in the information age. One
consequence is the creation of legislation in different jurisdictions that
address this topic, GDPR, LGPD, and PDP333https://prsindia.org/billtrack/the-
personal-data-protection-bill-2019 are a few examples. This legislation tries
to organise how data is treated and protected. To tackle the privacy problem
the SITA model was developed.

SITA [1] is a conceptual model that empowers end-users with the ability to
control their privacy. It is based on a granular approach to control privacy
in applications, and also, uses Maeda’s ten laws of simplicity [18] as a
design philosophy. As result, an end-user has a simple and intuitive method of
controlling how an application can distribute its data.

The embedded privacy control in most applications works binary, where a user
can block the application from sharing all his data or allow it to share all
his data in the application. SITA proposes the use of different levels as a
way to remediate this. The user can control how much information he is
sharing, this, however, comes with a cost. Less information that is shared in
the application can degrade an application service because of the lack of
precise information. As consequence, a user will need to set the application
privacy control in some way so that the application is usable for his needs
and also does not share so much information. This trade-off is very common in
privacy applications, known as the privacy-utility trade-off. Other frameworks
work on a similar premise, allowing more control over the privacy settings
[23] [10], however, these models are in general complex, which hinders their
widespread usage.

The model is divided into four dimensions: Spatial, referring to the user’s
location data, such as GPS position, address, and others; Identity, related to
the user’s personal identification data, such as ID, Name, and Gender;
Temporal, date and time information about user activity in the application;
Activity, sensitive data about user behaviour, situational data, and
preferences. All the data shared with the application developer can be
categorised into one of these four groups.

Each dimension can be assigned a level from zero to four. The level represents
the amount of privacy for that specific dimension. Where zero represents no
access to the data and complete privacy. On the other extreme four represents
full access and no privacy protection. The values in-between allow controlling
the shared data using aggregation, and obfuscation techniques to granularly
control privacy. In that case, the amount of information shared is something
between no shared data at all (level zero), and total access to the data
(level four). These in-between values are application specific and need to be
created by the developer.

An end user can set the level for each dimension, which is called a SITA
configuration. The resulting data shared will apply the privacy level for each
dimension and share the data with the application. A user can for instance set
The SITA level as Spatial two, Identity, three, Temporal zero, and Activity
four. The resulting shared data would include Spatial and Identity data
modified by some anonymization aggregation, and obfuscation techniques, all
the temporal data, and no activity data. In this work to identify different
configurations a sequence of four numbers is used. Each number represents the
value for one dimension. For example, 4343 means S = 4, I = 3, T = 4, and A =
3.

###  2.3 Supervised machine learning algorithms

Supervised machine learning algorithms use previously labelled data to train
machine learning models. One of its main uses is to allow the inference of
further data to a label. There are several algorithms, and variants, that use
this method [12]. For this work, we describe the algorithms used in our
experiment.

####  2.3.1 Linear regression (LR)

This method is used to predict the value of a variable, named the dependent
variable, and based on the value of other variables, named the independent
variables. The independent variables are used to compose a linear equation.
The dependent variable of a new data entry is predicted using the value of the
independent variables as input in the linear equation, resulting in the
predicted value.

####  2.3.2 Ridge Regression (RR)

Multiple regression models, similar to linear regression, create an equation
using independent variables to predict a dependent variable. However, unlike
linear regression, it creates an equation composed of multiple coefficients.
One problem that such an approach suffers is that highly correlated
independent variables degrade the model performance. Ridge Regression solves
this problem by substituting the least square estimators from multiple-
regression models with a ridge regression estimator.

####  2.3.3 Random forest (RF)

Random forest uses multiple decision trees to output a label for a new data
entry. The label which is outputted more by the decision trees is the one
attributed to the new data entry. Decision trees are multiple decision points
in a tree-like format. Each non-leaf node contains a decision that will induce
entry to a leaf node. The leaf node contains the label that the data entry
will assume. The multiple trees in the random forest are created with some
degree of variance. This method reduces the bias and overfitting that using a
single decision tree can result in.

####  2.3.4 Gradient Boosting Regressor (GBR)

This model is a variant of the ensemble methods, like RF. In these methods,
multiple simple models are combined into a more complex and precise one. For
GBR the simpler models usually are decision trees. The result is a more robust
model overall. This approach can find any non-linear relationship in the
dataset and can treat missing values, and outliers. The main difference
between RF and GBR is the process of creating the decision tree and combining
the results.

####  2.3.5 Decision Tree Regressor (DTR)

A decision tree is the smaller model that is used in the RF and GBR. It is a
classifier in the form of a tree. Each node is a decision and the leaves are
the labelled value. A new entry starting at the root is directed at each node
to children nodes based on its value. At the leaf node, it receives its label.
The tree is built by partitioning the training dataset and building a decision
node based on the partitioning.

##  3 Experiment

For our experiments, we collected data from a real scenario, the Urban
Sciences Building(USB) at Newcastle444https://api.usb.urbanobservatory.ac.uk..
In the next sections, we describe the methodology used for collecting and pre-
processing the data, applying the privacy model, and the metrics used to
measure the performance of the machine learning algorithms.

###  3.1 Privacy model for IoT datasets in $CO_{2}$ prediction

In IoT scenarios, data from sensors can be stored in datasets. A dataset can
be useful for history, use in prediction models and pattern detection,
forensics, and other cases. However, the dataset can also be used in a
malicious way. For example, a malicious data holder can use the data as part
of a linkage attack to infringe upon individual privacy. To mitigate this risk
one technique is to reduce the precision of data entries in the dataset. This
diminishes the data precision, hindering malicious privacy attacks. This
approach is used by the SITA model, from an original dataset it generates a
private dataset where the original data entries are less precise.

Figure 1: Proposed privacy model used for $CO_{2}$ prediction

Figure 1 presents how we apply the SITA model to add different privacy levels
to the original data. First, the data is collected from multiple sensors and
aggregated in the original dataset. This data will suffer a SITA
transformation based on a SITA Configuration. It is important to note that the
same configuration is applied to all the datasets, instead of a configuration
for a specific user’s data or data entries. The reason for this is that we
intend to isolate and analyse the impact of the transformation for each SITA
dimension on a machine learning $CO_{2}$ prediction model. The result of the
SITA Transformation is a private dataset, a dataset with increased security
against privacy attacks. This private dataset is then used to create a
prediction model using machine learning techniques.

This approach reflects the possibility that an IoT environment can
automatically transform an original dataset, or data as it is added to a
dataset using the SITA model. The resulting private dataset can be the only
information made available to the data holder. This guarantees an increase in
privacy. However, this comes with the cost of data utility, since the
modification to the original dataset will decrease its data utility and
prejudice the prediction models created from it.

###  3.2 Attack model

Here we present a simple model that can be used to exploit the $CO_{2}$
readings from a sensor, or its prediction if accurate, to determine which
specific person is in a room. There is a number of studies that suggest that
people who weigh more produce more $CO_{2}$ [19], and there are studies that
suggest that males produce more $CO_{2}$ than females [35]. Also, there is the
ASHRAE Standard 62.1[3] used to predict $CO_{2}$ emission inside a building
and control air quality, it receives as input the metabolic rate, which is
highly based on a person’s body composition (fat, muscle, bones, and etc.). In
our model, the potential difference of $CO_{2}$ emission between two
individuals will change the $CO_{2}$ readings of the room, allowing it to be
used to identify who is inside a room.

The scenario for this model is a small closed room that is used by just two
people. These two people have a significant difference in body composition and
are of different sex. We will identify them as Alice (50kg) and Bob (90kg), a
third person, Eve, wants to identify who is inside the room without their
consent. Eve has some background information, she knows the sex of Alice and
Bob and their approximate weight. Eve also has access to the $CO_{2}$ readings
of the room.

In Figure 2 we summarise our model. The $CO_{2}$ readings of the room (a), and
the background information (b) will be the input of the model of the gas
dispersion model (c), that model will as result identify who is inside the
room. The gas dispersion model can use the ASHRAE Standard 62.1[3] to predict
the $CO_{2}$ present in the room when it is empty, Alice is in it, Bob is in
it, or both. Using this prediction and the actual $CO_{2}$ readings Eve can
then predict who is in the room. The gas dispersion model is not the only
option viable for Eve, if she has historic data of the room $CO_{2}$ she can
use an unsupervised machine learning algorithm to cluster the readings in four
groups, group 1 when the room is empty, group 2 when Alice is in the room,
group 3 when Bob is in the room, and group 4 when both are in the room.
Finally, if Eve has a history of $CO_{2}$ readings and who is present in the
room even other supervised machine learning algorithms are viable.

This model is very simplistic and in more complex scenarios can be
ineffective. A few possible changes that can impact this model include:
Changing the number of people that use the room, this would increase the
complexity of isolating the $CO_{2}$ emission of one person, and more people
more complex. People with very similar profiles, same-sex same weight, or very
close. A big room would disperse the emitted $CO_{2}$, and the presence of
people inside the room would change very little in the room readings, making
it impossible to distinguish who is inside. Ventilation can also disperse the
$CO_{2}$, making it impossible to distinguish who is inside.

Figure 2: $CO_{2}$ individuals’ privacy attack model

###  3.3 Description of the Experiment

The objective of our experiment is to analyse the impact of the SITA privacy
model in $CO_{2}$ predictions that use machine learning. More specifically the
experiment aims to analyse how changing one dimension of the SITA
configuration impacts the performance of the prediction model. Privacy
techniques, such as SITA, suffer from the privacy-utility trade-off, increased
privacy decreases the data utility which will impact the prediction model.

We briefly summarise the experiment conducted in the following sequence of
steps and in Figure 3. More detail will be provided in the next subsections:

Figure 3: Experiment summary

  * •

Collecting original dataset: The first step was the collection of the dataset
for the prediction model. We used data publicly available from the USB.

  * •

Data Transformation: The original dataset was transformed to better suit the
SITA transformation.

  * •

SITA Transformation: Different datasets were created from different SITA
configurations. We aim to analyze the isolated impact of each SITA dimension.
Thus, for each dimension, we changed its level from 0 to 4 while keeping the
others in a fixed state. We will analyse the SITA configurations X444, 44X4,
and 444X, where X is a number between 0 and 4. This transformation results in
five different private datasets for each dimension.

  * •

ML Training: Our prediction models are created using LR, RR, RF, GBR, and DTR.
The selected techniques are based on the work of Wibisono et al. [33]. Each
technique is used with all datasets generated from the different SITA
configurations.

  * •

Analysis: To analyse the impact of a SITA configuration in the prediction
model we utilise common ML metrics. The metrics are $R^{2}$ score, Root Mean
Square Error(RMSE), and Mean Absolute Error (MAE). The chosen metrics are also
based on the work of Wibisono et al. [33].

###  3.4 Dataset Scenario and data collection

Our scenario is composed of the USB.It is the largest open platform for urban
sensing data in Newcastle. The building includes multiple IoT sensors that can
be freely accessed online through their website. An API is also provided,
which allows other applications to access the data, in real-time, and also
previous data.

The dataset was extracted using a script developed by our team, collecting
data from October 2018 to March 2020. We collected data from five sensors:
humidity, temperature, occupancy, brightness, and $CO_{2}$. The data is also
organised by rooms, with the readings of the sensors in each of these rooms.
The rooms differ in size, sensors available, and usage. The historical data is
stored in the API by sensors, in different JSON files. Therefore, we had to
consolidate all the data into a single file and remove all records that
contained at least one missing data.

###  3.5 Data Transformation

To remove all the outliers from our dataset, we set a range of values for each
feature. So we have $CO_{2}$ values ranging from 0 ppm to 1,000 ppm (ASHRAE
limit for healthy environments555https://www.ashrae.org/about/position-
documents) and temperature values ranging from 0°C to 50°C. The relative
humidity values ranged from 0% to 100% and the brightness values ranged from 0
lm to 2000 lm.

After completing the data cleaning as described above, we have a new dataset
with about 200,000 records. This dataset is ready to be used in the SITA
transformation and in future works.

###  3.6 SITA Data Transformation

Each level of the SITA parameters corresponds to a specific operation on those
variables related to that parameters. Below we describe those relationships,
and the transformations performed at each level. The Identity dimension is
absent in our work, since there is no individual data stored in the datasets,
and because of that its respective operations are disabled.

The Spatial dimension is represented by data regarding the room and the zone
of each entry. Given a sample input G.024, 2, the operations follow:

  * •

Level 0: all data deleted. Output: deleted,deleted

  * •

Level 1: only the general location is given. Output: building,deleted

  * •

Level 2: only the ground of each room is given. Output: Ground Floor,deleted

  * •

Level 3: returns full information about the room, omitting the zone data.
Output: G.024, deleted

  * •

Level 4: no transformations are applied. Output: G.024, 2

For the Temporal dimension, we took as input the datetime parameter. We
present the results given a sample input 20181011141735.

  * •

Level 0: all data deleted. Output: deleted,deleted

  * •

Level 1: year and month, with day fixed at 01. Output: 20181001,deleted

  * •

Level 2: date. Output: 20181011,deleted

  * •

Level 3: date and hour. Output: 20181011, 140000

  * •

Level 4: no transformations are applied. Output: 20181011, 142735

For the Activity dimension, we considered the following attributes: CO2,
Temperature, Humidity, and Brightness. For the sample input
287.0,27.6,63.8,25.0, we have the following operations:

  * •

Level 0: all data deleted. Output: deleted,deleted,deleted,deleted

  * •

Level 1: values are rounded up to the two rightmost digits. Output:
300,0,100,0

  * •

Level 2: values are rounded up to the rightmost digit. Output: 290,30,60,30

  * •

Level 3: decimal digits are removed. Output: 287,27,63,25

  * •

Level 4: no transformations are applied. Output: 287.0,27.6,
63.8,25.0

For the experiment, a SITA configuration is applied to all the entries in the
dataset. We applied the following configurations to the original dataset:
4444, 3444, 2444, 1444, 0444, 4434, 4424, 4414, 4404, 4443, 4442, 4441, and
4440. To better organise we will refer to a group of operations that alter the
same dimension using X for the dimension it is altering. For example, X444
refers to configurations 4444, 3444, 2444, 1444, and 0444. Note that this
transformation from the original dataset resulted in multiple private
datasets, one new dataset from each configuration.

###  3.7 Machine Learning training

The ML models were trained using the Kaggle666https://www.kaggle.com platform,
in a remote computing environment with 4 CPUs and 16 Gigabytes of RAM. The
library used for the training was the scikit-learn777https://scikit-learn.org
version 1.0.2. Before the training, since the algorithms here studied only
work with numerical data, we transformed all textual data into numerical over
each dataset. After this, the datasets were split into training/testing in a
proportion of 80/20 utilising random sampling, with a random state of 10. Over
these, we applied the KFold() method from scikit-learn, with ten splits and
setting the parameter shuffle to true, to avoid overfitting the model. Each
regressor method was then instantiated using their default implementations;
the $R^{2}$, MAE, and RMSE scores were calculated using the function
cross_val_score(), indicating in the score parameter the respective metric.

##  4 Results and Discussion

This section summarises the results of our experiments. We have three sets of
SITA configurations, each one varying by one dimension and keeping the other
three unmodified (i.e. level 4 of the model). We did this to better comprehend
the impact of applying different SITA options over the dataset.

We measured the results accordingly to three metrics: $R^{2}$ score, Mean
Absolute Error (MAE), and Root Mean Squared Error (RMSE). $R^{2}$ score, also
known as the Coefficient of Determination, measures the proportion of the
variance in the dependent variable that is predictable from the independent
variable(s). In other words, it represents the correlation between the
predicted outcomes of a model and their real values[7]. Mean Absolute Error is
the average difference between predicted and real values, representing how
much the model misses the expected value. The Root-mean-square error, like
MAE, is also an error metric, but here the measurement is calculated by the
square root of the average of squared differences between prediction and
actual observation. Although similar (with MAE being usually recommended, due
to its easier interpretation[34]), MAE and RMSE exhibit distinct behaviours in
specific circumstances, such as with large test sizes; thus, a combination of
both metrics are often required to assess model performance[6].

For each SITA configuration analysed, we applied ten-fold cross-validation
over the trained models, and collected their $R^{2}$, MAE, and RMSE values at
the end of each execution; after all executions were completed, we calculated
the average scores of each model/configuration pair.

Figure 4: $R^{2}$ score for machine learning algorithms in 444X SITA
dimension. Figure 5: $R^{2}$ score for machine learning algorithms in X444
SITA dimension. Figure 6: $R^{2}$ score for machine learning algorithms in
44X4 SITA dimension. Figure 7: MAE score for machine learning algorithms in
444X SITA dimension. Figure 8: MAE score for machine learning algorithms in
X444 SITA dimension. Figure 9: MAE score for machine learning algorithms in
44X4 SITA dimension. Figure 10: RMSE score for machine learning algorithms in
444X SITA dimension. Figure 11: RMSE score for machine learning algorithms in
X444 SITA dimension. Figure 12: RMSE score for machine learning algorithms in
44X4 SITA dimension.

For every specific setting, we ran all five algorithms, and measured the
results according to three parameters: the coefficient of determination
($R^{2}$ score - figures 2 to 4), the Mean Absolute Error (MAE - figures 5 to
7), and the Root Mean Squared Error (RMSE - figures 8 to 10). Since there are
significant performance differences between Random Forest and Decision Tree
models compared to the other algorithms evaluated, we will focus our analysis
on these first two.

Regarding the $R^{2}$ score, the Random Forest algorithm outperforms all other
approaches analysed, with an average value of 74.29% for the baseline data.
When taking the Activity dimension as variable, the minimum average score of
this model is 66.05%. This represents a performance decrease of 9.44%
regarding to the baseline. Even in this worst-case scenario, the Random Forest
model still outperforms the second-best algorithm (Decision Tree) by 23.62%,
even when considering the baseline for the latter. It is important to observe
that we did not apply level 0 of SITA transformations in that dimension since
this operation would erase all CO2 data, thus making it impossible to predict
its levels.

For the Temporal dimension, the minimum average score of Random Forest is
51.80% for the Temporal privacy setting of 0 (i.e. all data deleted). This
represents a performance decrease of 28.98% regarding the baseline. In this
worst-case scenario for the Random Forest model, the Decision Tree baseline
performance is 3.05% superior, with all other temporal settings of the Random
Forest performing better than the Decision Tree baseline, and all other
temporal settings of the Decision Tree underperforming the Random Forest
worst-case.

When considering the Spatial dimension, the Random forest model produces an
average $R^{2}$ score of 71.86% when the privacy setting is at 0, a decrease
of 1.73%. This value is 33.00% higher than the Decision Tree score for the
baseline case, having this model in the same privacy level a score of 51.16%,
being 28.81% lower than RF.

Analysing the Mean Absolute Error, the lowest score for the baseline was
obtained running the Random Forest model, with a result of 32,40%. With the
Activity dimension as variable, the maximum average score of such algorithm
equals to 41,28% when privacy level = 1 for Activity. This represents an error
increase of 27,38% regarding to the baseline. The Decision Tree algorithm
produces an MAE value of 40,60%, which represents a decrease of 1,64% in
relation to the Random Forest.

For the Temporal dimension, we have a maximum average score of 49,85% (privacy
level = 0), 53,50% higher than the baseline. The same configuration when
applied to Decision Tree produces an MAE score of 50,93%, 2,17% higher than
the Random Forest model.

The Spatial dimension has an MAE score of 33,63% on the strictest privacy
level for the RF model. This represents an increase of 3,56%, considering the
baseline. The Decision Tree model, conversely, has an MAE score of 35,49%, an
increase of 5,53% from the RF.

Finally, we look at the RMSE metric. The lowest value was obtained with the
Random Forest model, with a score of 62.99%. For the Activity dimension, the
highest value was 75,47%, 13.83% higher than the baseline. When we set
Temporal and Spatial domains as variables, their respective scores were 84.20%
(an increase of 33.67%) and 64.31% (2.09% higher than the baseline).

###  4.1 Discussion

Firstly, our results confirm the experimental data presented in [33],
regarding the performance of the ML algorithms then analysed. Linear
Regression and Ridge Regression, being fairly simple algorithms , are expected
to perform poorly than more sophisticated methods, especially on large
datasets. The other algorithms used in our experiments can be seen as
belonging to the same family, with Decision Tree being the basis for both
Random Forest and Gradient Boosting. However, some careful tuning is necessary
for the latter to achieve good results, which makes it harder to apply the
method over different domains. Regarding Decision Tree and Random Forest
methods, since the latter is an averaging of multiple instances of the first
(thus mitigating possible errors due to overfitting), the results of our
experiments confirm the expected performances. We also show that RF is the
only algorithm between those analysed that produces $R^{2}$ scores over 70%.
When analyzing the Mean Absolute Error, our results show that the performances
of RF and DT algorithms are very close, but the RMSE values present a more
significant difference between these two methods. This can be explained by the
fact that RMSE has a tendency to be increasingly larger than MAE as the test
sample size increases, thus exacerbating small differences in MAE values
between the two approaches.

Another discussion can be made about the impact of applying our SITA
implementation over the chosen dataset. Our results show that the dimensions
present different sensibilities to more restrictive privacy settings. Taking
the $R^{2}$ score, we show that the Spatial dimension is the least affected,
and the Temporal dimension the most affected, with Activity being in an
intermediate place. This can be used to better understand the importance of
different variables in applying ML techniques. Also, by analysing the scores
of each privacy setting, we observe that the Activity dimension has a score
below 70% when the privacy setting is lower than 3; the same occurs for the
Temporal dimension with privacy setting lower than 4 (reflecting the higher
sensitivity of this dimension), and it is not observed in the Spatial
dimension in any configuration. with this we demonstrate that it is possible,
through different SITA settings, to improve the users’ privacy and keep ML
services functional.

An interesting approach for further research on this topic is the use of other
machine learning algorithms, including more powerful techniques such as deep
learning. Exploring other domains such as healthcare, social media, and other
IoT scenarios for example are also interesting further directions.

##  5 Related Work

There are numerous works related to the prediction of $CO_{2}$ in IoT
environments using machine learning algorithms. The $CO_{2}$ monitoring is an
important component of controlling the air quality of a room, which when
correctly managed provides well-being, controls general air pollution, and
detects potential harms, such as fire. Creating a prediction model can be
positive in cases presented by Kapoor et al. [14] where smart sensors are not
available, also a model can be used to help in the building design. In his
work, they present a model working with multiple machine learning algorithms
and achieve a precise model.

Other works are developed in a similar fashion using machine learning
algorithms in an IoT scenario to create a prediction model for $CO_{2}$. Vanus
et al. [31] use the value of other sensors like temperature and humidity to
predict the $CO_{2}$ value in a Smart Home scenario. In another study, Sharma
et al. [26] describe the building of a sensor network to detect different
pollutant gases beyond $CO_{2}$, although still in development a model is
described. An artificial neural network is used to predict air quality and to
fully control an IoT network, including air-conditioning, and ventilation
based on the work of Tagliabue et al. [29].

There are other works in the prediction of air quality and $CO_{2}$ [30] [13]
[15]. However, the use of such prediction models and privacy models as the one
presented in Section 2.2 are not common. The readings of a $CO_{2}$ with other
background information can be used in a linkage attack [20] in the original
dataset, or real-time data, to discover information about individuals, for
example, who was present in a room, patterns of movements in a building, among
others.

##  6 Final consideration and future work

In this work, we analysed the trade-off between privacy and utility for
$CO_{2}$ prediction on a real dataset in the context of smart buildings.
Therefore, several transformations were implemented on the original data to
simulate different privacy levels and generate new transformed datasets that
were used as input to train five distinct machine learning models for $CO_{2}$
prediction.

The results show that the performance of Regression based machine learning
techniques is lower than decision Tree-based techniques. The use of the
privacy model, as expected, deteriorated the performance of all algorithms.
More aggressive SITA configurations resulted in worse performance and each
dimension has a different impact on the prediction models. The highest impact
was observed when higher privacy levels were simulated on the Temporal
dimension.

As future research directions, our model could be improved by using Syntactic
Anonymity [28] with SITA to increase even more the data privacy. To the best
of our knowledge, there is no work of this kind yet. Also, the inclusion of
Differential Privacy [8] is another possibility that could improve even more
the privacy model, since it is a more powerful privacy definition than
syntactic anonymity.

## References

  * [1] Andersen, M.S., Kjargaard, M.B., Grønbæk, K.: The sita principle for location privacy — conceptual model and architecture. In: 2013 International Conference on Privacy and Security in Mobile Systems (PRISMS). pp. 1–8 (2013). https://doi.org/10.1109/PRISMS.2013.6927184
  * [2] Arif, M., Katafygiotou, M., Mazroei, A., Kaushik, A., Elsarrag, E., et al.: Impact of indoor environmental quality on occupant well-being and comfort: A review of the literature. International Journal of Sustainable Built Environment 5(1), 1–11 (2016)
  * [3] Ashrae, A., Standard, A.: 62.1. 2007, ventilation for acceptable indoor air quality. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta, GA (2007)
  * [4] Buckman, A.H., Mayfield, M., Beck, S.B.: What is a smart building? Smart and Sustainable Built Environment (2014)
  * [5] Candanedo, L.M., Feldheim, V.: Accurate occupancy detection of an office room from light, temperature, humidity and co2 measurements using statistical learning models. Energy and Buildings 112, 28–39 (2016)
  * [6] Chai, T., Draxler, R.R.: Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature. Geoscientific model development 7(3), 1247–1250 (2014)
  * [7] Draper, N.R., Smith, H.: Applied regression analysis, vol. 326. John Wiley e Sons (1998)
  * [8] Dwork, C.: Differential privacy: A survey of results. In: Proceedings of the 5th International Conference conference on theory and applications of models of computation (TAMC). pp. 1–19 (2008)
  * [9] GhaffarianHoseini, A., Dahlan, N.D., Berardi, U., GhaffarianHoseini, A., Makaremi, N., GhaffarianHoseini, M.: Sustainable energy performances of green buildings: A review of current theories, implementations and challenges. Renewable and Sustainable Energy Reviews 25, 1–17 (2013)
  * [10] Gupta, A., Kalra, A., Boston, D., Borcea, C.: Mobisoc: a middleware for mobile social computing applications. Mobile Networks and Applications 14(1), 35–52 (Feb 2009). https://doi.org/10.1007/s11036-008-0114-9, https://doi.org/10.1007/s11036-008-0114-9
  * [11] Jacobson, T.A., Kler, J.S., Hernke, M.T., Braun, R.K., Meyer, K.C., Funk, W.E.: Direct human health risks of increased atmospheric carbon dioxide. Nature Sustainability 2(8), 691–701 (2019)
  * [12] Jiang, T., Gradus, J.L., Rosellini, A.J.: Supervised machine learning: A brief primer. Behavior Therapy 51(5), 675–687 (2020). https://doi.org/https://doi.org/10.1016/j.beth.2020.05.002, https://www.sciencedirect.com/science/article/pii/S0005789420300678
  * [13] Kadam, P., Vijayumar, S.: Prediction model: Co2 emission using machine learning. In: 2018 3rd International Conference for Convergence in Technology (I2CT). pp. 1–3 (2018). https://doi.org/10.1109/I2CT.2018.8529498
  * [14] Kapoor, N.R., Kumar, A., Kumar, A., Kumar, A., Mohammed, M.A., Kumar, K., Kadry, S., Lim, S.: Machine learning-based co2 prediction for office room: A pilot study. Wireless Communications and Mobile Computing (Mar 2022)
  * [15] Khorram, M., Faria, P., Abrishambaf, O., Vale, Z., Soares, J.: Co2 concentration forecasting in an office using artificial neural network. In: 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP). pp. 1–6 (2019). https://doi.org/10.1109/ISAP48318.2019.9065944
  * [16] Labeodan, T., Zeiler, W., Boxem, G., Zhao, Y.: Occupancy measurement in commercial office buildings for demand-driven control applications—a survey and detection system evaluation. Energy and Buildings 93, 303–314 (2015)
  * [17] Lam, K.P., Höynck, M., Dong, B., Andrews, B., Chiou, Y.S., Zhang, R., Benitez, D., Choi, J., et al.: Occupancy detection through an extensive environmental sensor network in an open-plan office building. IBPSA Building Simulation 145, 1452–1459 (2009)
  * [18] Maeda, J.: The Laws of Simplicity. Simplicity: Design, Technology, Business, Life, MIT Press (2006), https://books.google.com.br/books?id=lh9gRQ4i_zwC
  * [19] Magkos, F., Tetens, I., Bügel, S.G., Felby, C., Schacht, S.R., Hill, J.O., Ravussin, E., Astrup, A.: The environmental foodprint of obesity. Obesity 28(1), 73–79 (Dec 2019). https://doi.org/10.1002/oby.22657, https://doi.org/10.1002/oby.22657
  * [20] Merener, M.M.: Theoretical results on de-anonymization via linkage attacks. Trans. Data Privacy 5(2), 377–402 (aug 2012)
  * [21] Naeini, P.E., Bhagavatula, S., Habib, H., Degeling, M., Bauer, L., Cranor, L.F., Sadeh, N.: Privacy expectations and preferences in an $\\{$IoT$\\}$ world. In: Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017). pp. 399–412 (2017)
  * [22] Redlich, C.A., Sparer, J., Cullen, M.R.: Sick-building syndrome. The Lancet 349(9057), 1013–1016 (1997)
  * [23] Roman, M., Hess, C., Cerqueira, R., Ranganathan, A., Campbell, R., Nahrstedt, K.: A middleware infrastructure for active spaces. IEEE Pervasive Computing 1(4), 74–83 (2002). https://doi.org/10.1109/MPRV.2002.1158281
  * [24] Sarwar, K., Yongchareon, S., Yu, J., Ur Rehman, S.: A survey on privacy preservation in fog-enabled internet of things. ACM Comput. Surv. 55(1) (nov 2021). https://doi.org/10.1145/3474554, https://doi.org/10.1145/3474554
  * [25] Seliem, M., Elgazzar, K., Khalil, K.: Towards privacy preserving iot environments: A survey. Wireless Communications and Mobile Computing 2018, 1032761 (Nov 2018). https://doi.org/10.1155/2018/1032761, https://doi.org/10.1155/2018/1032761
  * [26] Sharma, P.K., De, T., Saha, S.: Iot based indoor environment data modelling and prediction. In: 2018 10th International Conference on Communication Systems & Networks (COMSNETS). pp. 537–539 (2018). https://doi.org/10.1109/COMSNETS.2018.8328266
  * [27] Smith, S.: Intelligent buildings. In: Design and Construction, pp. 60–82. Routledge (2007)
  * [28] Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(05), 571–588 (2002). https://doi.org/10.1142/S021848850200165X
  * [29] Tagliabue, L.C., Re Cecconi, F., Rinaldi, S., Ciribini, A.L.C.: Data driven indoor air quality prediction in educational facilities based on iot network. Energy and Buildings 236, 110782 (2021). https://doi.org/https://doi.org/10.1016/j.enbuild.2021.110782, https://www.sciencedirect.com/science/article/pii/S0378778821000669
  * [30] Vanus, J., Krestanova, A., Kubicek, J., Gorjani, O., Penhaker, M., Oczka, D.: Using wavelet transformation for prediction co2 in smart home care within iot for monitor activities of daily living. In: Nguyen, N.T., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds.) Computational Collective Intelligence. pp. 500–509. Springer International Publishing, Cham (2019)
  * [31] Vanus, J., Martinek, R., Bilik, P., Zídek, J., Dohnalek, P., Gajdos, P.: New method for accurate prediction of co2 in the smart home. In: 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings. pp. 1–5 (2016). https://doi.org/10.1109/I2MTC.2016.7520562
  * [32] Verma, A., Prakash, S., Srivastava, V., Kumar, A., Mukhopadhyay, S.C.: Sensing, controlling, and iot infrastructure in smart building: A review. IEEE Sensors Journal 19(20), 9036–9046 (2019). https://doi.org/10.1109/JSEN.2019.2922409
  * [33] Wibisono, A., Wisesa, H.A., Habibie, N., Arshad, A., Murdha, A., Jatmiko, W., Gamal, A., Hermawan, I., Aminah, S.: Dataset of short-term prediction of co2 concentration based on a wireless sensor network. Data in Brief 31, 105924 (2020). https://doi.org/https://doi.org/10.1016/j.dib.2020.105924
  * [34] Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate research 30(1), 79–82 (2005)
  * [35] Yang, L., Wang, X., Li, M., Zhou, X., Liu, S., Zhang, H., Arens, E., Zhai, Y.: Carbon dioxide generation rates of different age and gender under various activity levels. Building and Environment 186, 107317 (2020). https://doi.org/https://doi.org/10.1016/j.buildenv.2020.107317, https://www.sciencedirect.com/science/article/pii/S0360132320306880