Incorporating Privacy Requirements in Smart Communities for Older
Adults: A Research Vision
Fauzia Khan
and Ishaya Gambo
Institute of Computer Science, University of Tartu, Narva mnt 18 51009 Tartu, Estonia
Smart City, Privacy Requirements, e-Healthcare System, Requirements Engineering.
In recent years, socio-technical systems like smart city technology have received growing interest. Privacy re-
quirements in smart technologies hold significant importance, but it is difficult to elicit by traditional require-
ments elicitation techniques as several contextual factors are involved. Therefore, these techniques cannot be
effectively used to analyze privacy requirements. Our study aims to develop a framework that elicits privacy
requirements of older adults in smart communities and to improve the privacy awareness of individuals in
social groups. Our proposed framework is applied to a hypothetical scenario of an older adult using a smart
e-healthcare system to analyze privacy requirements and make users aware of whom they are sharing their
information with in social groups.
In computing and socio-technical systems (e.g., smart
cities), privacy is a fundamental concern that neces-
sitates the amalgamation of technical and social per-
spectives in chatting the way forward for realizing
positive solutions. Most organizations and systems
are faced with privacy problems. For example, in the
healthcare domain, keeping patient’s medical records
and information private is an issue of concern.
Overall, privacy requirements engineering aims to
create systems that safeguard people and data while
taking into account changing risks, situations, and re-
quests in the context of use. The significant problem
is that already existing technologies, such as smart-
phones and wearable devices, have been successfully
used to define smart cities. Although providing su-
perior solutions in terms of ease of use and service
delivery, these technologies are also being utilized to
invade people’s privacy. These technologies exist in
a context, not in isolation. It could be in a social,
health, or educational environment where the tech-
nologies are used to socialize or augment socializa-
Until recently, smart city technology has received
growing interest, especially in the healthcare domain.
The most significant purpose of a smart city is to im-
prove social or economic inconvenience and maxi-
mize social inclusion by using smart technologies and
data analysis (Harmon et al., 2015). However, its
complex nature has raised significant technical, politi-
cal, and socioeconomic challenges for designers, inte-
grators, and organizations involved in administrating
this technology (Ismagilova et al., 2020). In actuality,
smart cities are built to upgrade the quality of life for
ordinary citizens. For example, the residents of smart
homes can control their ventilation, lighting, heating,
cooling, and automatic door lock facilities. Also, they
can avail themselves of the benefits of security and
In context, a smart city offers several facilities
like efficient transport, infrastructure, crime preven-
tion, traffic control, power, and water distribution (Pe-
ters et al., 2018). These services require capturing,
storing, and processing personally identifiable data,
which raises privacy issues. Ignoring privacy con-
cerns will lead to technology rejection and eventually
make unhappy users (Curumsing et al., 2019; Thielke
et al., 2012). Hence it is important to consider pri-
vacy requirements to make smart communities more
acceptable, specifically among older adults.
Privacy is both social and legal issue that holds
significant importance. In modern society, privacy
is an enabler of monitoring and searching informa-
tion (Thomas et al., 2014). The modern society in
this context is characterized by several individuals
having to share personal information with different
Khan, F. and Gambo, I.
Incorporating Privacy Requirements in Smart Communities for Older Adults: A Research Vision.
DOI: 10.5220/0011319100003266
In Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022), pages 242-249
ISBN: 978-989-758-588-3; ISSN: 2184-2833
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
organizations, institutions, web services providers,
and online social networks (OSN). An example of
such an institution is the healthcare system that is
information-intensive, process- and service-oriented
(Gambo et al., 2011; Gambo et al., 2014; Gambo and
Soriyan, 2017).
On the one hand, eliciting privacy requirements,
for example, in older adults’ smart homes, can be
challenging from a requirement engineering (RE) per-
spective. In particular, designing standard techniques
for eliciting privacy requirements is considered not
to be straightforward and crystal clear because of the
following reasons. First, understanding the user per-
spectives about sharing data differs from each other,
e.g., social media updates are highly sensitive data for
some people while others are less concerned about
sharing it with others.
Secondly, privacy requirements are difficult to ex-
press due to the nature of people. In most cases,
people are unclear when expressing their privacy,
especially during the RE process. Therefore, it is
challenging for requirements engineers to recom-
mend standard techniques for eliciting privacy re-
quirements. Thirdly, in the context of smart cities,
many ubiquitous information technologies are in-
volved, e.g., clouds, wearable devices, sensors, and
smartphones. They all have contextual factors be-
cause standard techniques could not be effective.
On the other hand, people’s social behavior could
influence eliciting software privacy requirements.
People usually have different behavior and individual
privacy policies, but what happens when individuals
join groups like social groups, family groups, aca-
demic groups, etc. Usually, in groups, people are
not much concerned about what they have shared,
knowing who can see it. When people join a group,
they will have to follow the policies and rules of that
group. To understand this, we introduced the notion
of privacy dynamics to understand group dynamics,
especially on how people behaved in groups and tried
to use the individual privacy policy of people to learn
and improve the collective privacy awareness of the
group. For this purpose, we formulate two research
RQ1: How can we build an approach that cap-
tures learning new privacy dynamics within a speci-
fied system boundary and context?
RQ2: How can we use individual privacy to
learn and improve the group’s collective privacy
The RQ1 and RQ2 are answered empirically in
Sections 3 and 4, respectively, but the contribution
is defined as a step-by-step method, not its applica-
tion. In answering RQ1, a framework is proposed to
capture privacy requirements. We considered a hy-
pothetical scenario of an older adult using the smart
e-healthcare domain and modeled its whole informa-
tion flow, analyzed the privacy problem, and cap-
tured the privacy requirements as reflected in Section
4. To answer RQ2, we proposed creating an infor-
mation database and sharing recommendation gener-
ator, which suggests to the user whether to share or
not share data with a specific member within a group
based on his actions performed in history. The pro-
posed framework is discussed in section 3 and illus-
trated via an example in section 4 by extending the
same scenario used for RQ1. Summing up, the fol-
lowing are the main contributions made in this paper:
We propose a strategy for eliciting privacy re-
quirements from different contextual perspec-
tives, considering that smart cities have other ap-
plication domains that deal with the environment.
For this purpose, we developed a framework to
elicit privacy requirements for smart city domains
and learn how individuals’ privacy in a group
can affect the collective privacy awareness of the
We propose a framework to identify privacy
threats and dimensions to derive privacy require-
ments by modeling the information flow for the
system. We also identified other harms that pri-
vacy violations could produce.
We propose a framework to provide privacy
awareness by understanding group dynamics and
user social behavior by suggesting sharing rec-
ommendation generator followed by information
The rest of this paper is arranged as follows. Sec-
tion 2 discusses some related work and its limitations.
Section 3 presents our proposed approach. Section 4
illustrates an example of a case study to describe how
our approach can be used. Section 5 states the con-
clusion and future work.
The literature has proved that privacy issues have a
long history dated far back to 1890. In particular, it
is ”the right to be left alone” (Brandeis and Warren,
1890) and ”the right to select what personal infor-
mation about me is known to what people” (Westin,
1968). Also, in the modern world of mobile, ubiqui-
tous, adaptive, service-oriented, and human-centered
Incorporating Privacy Requirements in Smart Communities for Older Adults: A Research Vision
systems, the literature on privacy concern has pro-
vided to a great extent some levels of satisfaction for
the business and social needs of users and enterprises.
Thus, Omoronyia et al. (2013) observed that these
applications enable users to form localized, short and
long lived groups or communities to achieve their
common objectives. The behavioral nature of these
applications will involve the collection, dissemina-
tion, and even disclosure of sensitive information,
which threatens users’ privacy when exposed in an
unregulated manner (Langheinrich, 2002; Omoronyia
et al., 2013). This is the reason behind the failure
of systems that cannot provide satisfactory privacy
awareness requirements.
Further, the concept of adaptive privacy as a sys-
tem’s ability to preserve privacy in the presence of
context changes was introduced in (Schaub et al.,
2012) to cater for group dynamics. These changes
could be due to several expectations from users in a
group, which changes all the time, and the predomi-
nant behavior of users that enforces the change. How-
ever, the notion of a group is quite fundamental to how
privacy is being managed. For example, groups have
some knowledge or wisdom that can be exploited. So,
trying to understand the group property of privacy is
Calikli et al. (2016) presented a privacy dynamics
architecture inspired by social identity theory. The re-
search was based on a formal model with the concepts
of group membership information, represented as so-
cial identity maps, and privacy norms, represented as
a set of conflicts. Also, the research used the Induc-
tive Logic Programming (ILP) to learn a user’s pri-
vacy norms through examples of their sharing behav-
ior (Calikli et al., 2016). However, Calikli et al.s
(2016) work was based on assumptions, and the ILP
was subjected to learning user’s privacy norms as real
users sharing behavior were not used.
Additionally, few studies are available on elicit-
ing smart cities’ privacy requirements. For example,
(Miller et al., 2012) used interviews and question-
naires but found it challenging to capture the actual
privacy concern. Also, (Taveter et al., 2019) used
the motivational goal modeling approach to capture
stakeholders goals, including some privacy concern.
Their specific focus was on the healthcare domain that
was based on two case studies related to the e-health
sector in Estonia and Australia. Unfortunately, mul-
tidisciplinary skill sets are required to use it for their
proposed approach.
Moreover, Taveter et al. (2019) had blended a
top-down approach with a bottom-up approach which
could create a problem regarding viewpoint decision,
especially on determining which viewpoint is right or
wrong. A 2x2 framework is proposed to gather pri-
vacy concerns for smart cities in (Van Zoonen, 2016).
One dimension shows people’s more sensitive data
than others, whereas the other dimension shows peo-
ple’s privacy concerns regarding the purpose of data
being collected.
The author in (Van Zoonen, 2016) showed the ap-
plicability of the proposed framework and gave clear
directions for doing empirical research about privacy
concern in smart cities. (McNeill et al., 2017) focused
on the importance of privacy concerns of older adults
in the healthcare domain. They used thematic analy-
sis to identify six reasons older adults need privacy
and concluded that the designer should incorporate
privacy goals at the beginning of the designing phase
but did not discuss the design solution with their par-
ticipants whether the solutions are plausible or not.
Worthy of mention is the work by (Mart
e et al., 2013) on smart cities that defined five
dimensions of the privacy model. Still, traditional
techniques for eliciting privacy requirements are not
effective for the following reasons.
1. There is limited knowledge on capturing privacy
requirements and incorporating them in the soft-
ware design as every domain in smart cities has
dynamic characteristics, user involvement, het-
erogeneity, and scalability. Therefore, it is neces-
sary to be aware of privacy threats and concerns
when designing a new system or extending the
current system.
2. User behavior varies based on the scenario. For
example, a user may want to share his mobile
screen with his friend while sitting in a cafe. Still,
he will not be comfortable sharing it with the pas-
senger sitting next to him in a bus, or he may share
his photos with friends but with no teachers in his
university social group.
To fill this gap, we proposed a framework that could
be effective for any smart city domain to elicit privacy
threats and concerns by modeling its information flow
and making the user aware by providing suggestions
on whether to share information with a particular in-
dividual in a group or not.
We employed the qualitative and deductive ap-
proaches in SE (Runeson et al., 2012) to address the
research questions. These approaches are descrip-
tive and explanatory, using a hypothetical older adult
smart homes scenario. To address RQ1, we formu-
lated a framework called Privacy Requirements and
ICSOFT 2022 - 17th International Conference on Software Technologies
Figure 1: Framework for Privacy Requirements.
Awareness (PRA) in a few steps, as shown in Figure 1.
As Figure 1 reflects, our framework consists of
four phases: (i) Smart City Domains, which provides
smart solutions across all sectors, so we categorized
smart city sectors in smart mobility, buildings, envi-
ronment, governance, economy, health care defines
the different domain of the smart city. (ii) Modelling
Information Flow. In particular, as information flows
between different users and have different flow path
with a different purpose, we modeled the information
flow to identify roles, relations, and flow of informa-
tion between actors. (iii) Analyzing Privacy Problem.
This phase identifies privacy threats and concerns in
the flow of information. (iv) User Privacy Require-
ments - To capture and categorize user privacy needs.
3.1 Smart City Domains
Smart city domains refer to different systems like
smart transport systems, smart health care systems,
smart buildings, etc. ”Here, we noted that ascertain-
ing privacy solutions for smart cities is a difficult and
error-prone task because their heterogeneity and com-
plexity have limited the traditional requirements engi-
neering methodologies to elicit or capture stakehold-
ers’ privacy expectations”. As observed in (Thomas
et al., 2014), using traditional means to elicit pri-
vacy requirements is hard and incredibly impractical
for mobile, ubiquitous, service-oriented, and human-
centered systems. Remarkably, smart city domains
are highly context-dependent, and privacy require-
ments change from domain to domain and from an
end-user perspective. For example, in the healthcare
domain, the goal of smart health is to educate pa-
tients about their medical status and keep them health
aware. The kind of data in this domain includes pa-
tient health data and survey data. We focus more
on the older adults’ privacy requirements for this re-
search. Other smart city domains are Smart Mobility,
Smart Utilities, Smart Buildings, Smart Environment,
Smart Traffic Systems, Smart Governance, and Smart
3.2 Information Flow Model
In this phase, the flow of information in the smart
city domain must be identified, and how it is dissem-
inated to other users should be established. For that,
we identified four facets:
1. Information Facet: The information facet entails
categorizing the type of information, the purpose
of use, and the subscriber who can receive the in-
formation. This is to model what type of informa-
tion is involved and to whom it is disseminated to
other users, and the purpose of using the informa-
(a) Type of Information: Type of Information:
The focus is on knowing whether the infor-
mation is personal or confidential. For that,
the information can be a sensitive information,
which can be user-related information, such as
date of birth, medical records, mobile num-
ber, civic data, age, etc. This information can-
not be made public without the consent of the
owner. On the other hand, the information can
be insensitive information, user-related infor-
mation, such as name, gender, or other infor-
mation that can be made public with its owner’s
(b) Purpose: The focus is to identify for what
purpose data is being collected and how these
data pieces are used. As described in (Bha-
tia and Breaux, 2017), the purpose of data can
be for: a) Service, which includes any pur-
pose for which a company uses users’ data to
improve their services, e.g., search results, ad-
vertisements, or location-based services.b) Le-
gal purpose, which includes any legal purpose
regarding following the court notifications or
any other litigation. c) Communication pur-
pose that includes any purpose regarding com-
munication with users to address different pur-
poses, e.g., products, product updates, and ser-
vices, etc., d) Protection purpose that includes
any purpose related to fraud, data manipulation,
protection, and misuse e.g., to detect fraud in fi-
nancial services like credit cards. e) Merger
purpose that includes any purpose regarding
mergers or transferring control and property to
others. f) Vague purpose that includes any pur-
pose whose reason and consequences are un-
clear or any purpose which is not covered by
other mentioned purposes above.
Incorporating Privacy Requirements in Smart Communities for Older Adults: A Research Vision
(c) Subscriber: The emphasis is on whom infor-
mation need to be shared with, and to ascertain:
i. Whether others could see or receive personal
information or activities about users, thereby
informing users who can see their informa-
ii. What is the medium used for data collection?
Is data collected manually by end-users or by
computer automation?
2. Actor Facet: Identifying users with whom the
system interacts. For example, in the context of
the health care system, patients and doctors are
3. Role and Responsibility Facet: Identifying the
roles and responsibilities of actors (sender, re-
ceiver, and subject) holds significant importance
to ensure privacy. For example, in the taxi sys-
tem, the details of pickup and destination location
of a passenger may be required by the driver. So
both the driver and passenger with their relation-
ship and responsibility should be defined clearly.
4. Data Flow Facet: It is important to know how
data flow between actors to examine the privacy
requirements. The following question can be used
to elicit privacy factors.
(a) Is there any system and third party involved
who are recipients of information?–Determines
the information flow to a third party who can
exploit data.
(b) To identify the relationship between subjects
and system and third party?
(c) How subject feel in relation to software which
they are using i.e. trust relationship.
5. Owner, Consent and Permission: To identify
that who can control the use of sensitive infor-
mation? Owns: An actor who is legal owner of
data, Permission: An actor has full control to use
the information which he owns. Consent: is a le-
gal agreement between subject and another actor,
who can use the information with specific purpose
of use shown in agreement.
3.3 Privacy Problem Analysis
There are eight types of privacy according to study
(Friedewald et al., 2013) which include: Privacy
by person (genetic code, bio metric codes) Loca-
tion (traces, spatial-temporal data), Media (audio, im-
age, video), Behavior, and action (hobbies, purchases,
habits), Social life (interactions, contacts) State of
mind and body (thought, health opinion) association
(group privacy), communication (email, phones).
For analysis purposes of privacy requirements and
for sake of simplicity, first a generic architecture for
the flow of information between sender and receivers
was considered so that we can identify different pa-
rameters involved in information flow to elicit privacy
requirements. Usually, the information flows in three
steps as follows (Doyle, 2011): Firstly, the sender
sends information to a service provider and service
provider stores it. Secondly, receivers requests the
data from a service provider. At last, Upon request,
the service provider sends information to receiver. In
above mentioned information flow, following param-
eters contributed in order to elicit privacy require-
1. Risk Dimension: A potential incident which can
cause negative impact on overall software e.g
threats and vulnerability which can exploit user
2. Vulnerability: A weakness of a system which can
be exploited by a threat actor i.e to perform unau-
thorized actions.
3. Privacy Violation: It occurs when any informa-
tion flow causes harm to user. Harmful activi-
ties could occur in Loss of (Reputation, Freedom,
Finance, Anonymity, Relationship, Emotional
Harm, Embarrassment, Discrimination, Black-
mailing, Criminal Offence etc).
4. Privacy Determinants: To Identify component
which affects privacy in information flow.
5. Privacy Threats: To map the path of information
flow in software from where users can suffer harm
and identify the gaps in the requirement model
of current software. According to (Solove, 2005)
there are four basic groups of infraction flow
which can cause harmful activities.1)Information
collection, 2)information processing, 3)Informa-
tion dissemination, 4)Invasion. Few examples of
threats could be related to Surveillance, Interroga-
tion, Aggregation, Identification, Insecurity, Prox-
imal access, Secondary use, Breach of Trust, Mis-
information, Power Imbalance, Interference, and
Cross Contextual Information.
6. Privacy Goal: To counter threats in order to make
information protected and secured.
7. Privacy Constraint: To introduce restriction in
design of software to achieve privacy goals. It
is achieved by introducing privacy policy (actions
of actors which are allowed or prohibited to do)
and privacy mechanism (technique to implement
to achieve privacy goals).
ICSOFT 2022 - 17th International Conference on Software Technologies
3.4 Privacy Requirements
To address privacy threats and violations by pro-
viding feedback and control facilities so that user
can have better control over the information flow
and thus improve the privacy requirements of
the software.According to (Dritsas et al., 2006;
Gharib et al., 2021), privacy requirements are fur-
ther refined into eight concepts:
(a) Confidentiality: It means to incorporate all
necessary actions to keep user information in
accessible in case of any threat.
(b) Authentication: It means to incorporate mech-
anisms to verify who is accessing subject’s in-
(c) Authorization: It means to incorporate mech-
anism to verify whether actors have permission
to access the subject’s information with their
(d) Notice: To send data notice to subject when its
information has been collected. Also a viola-
tion notice should be send in case the subject
does not permits to do so.
(e) Anonymity: To keep the activities without dis-
closing the user identity of subject i.e. to re-
move identifiers like name , address, etc.
(f) Unlinkable: To make subjects unable to asso-
ciate information to its subject or to determine
whether the same user caused certain specific
operations in the system.
(g) Unobservant: It requires that subjects cannot
determine whether an activity or operation is
being performed.
(h) Accountability: It requires that company who
collect or use personal data take responsibility
for its protection and appropriate use.
To address RQ2, we divide our proposed approach
in two parts:
1. Information Database: In this step, we aim to
develop a repository that contains user names,
names of other group members (friends, col-
leagues, family) sharing objects like (images, sta-
tus, reports) and user sharing history and conflict.
For example user shared his photos with friends
but not with his class teacher in classroom group.
2. Recommendation Generation: After gathering
the whole information a recommendation genera-
tor will maps conflicts by analysing history of user
and will send sharing recommendation to user ev-
ery time when user is going to share something
on group. Figure 2 shows block diagram for pro-
posed approach.
Figure 2: Group Dynamics to Make Privacy Awareness.
In this section, we illustrated a hypothetical scenario
of independent old adult in healthcare domain. Due
to the age factor, older adults become weak men-
tally and physically, thus dependent on others. How-
ever, they want to live in their homes independently.
For this purpose, they used smart technology regard-
ing health domains like a smartwatch, bed, auto-
generated alarms, etc. They can be monitored con-
tinuously by health experts without face-to-face in-
teraction. In a smart health care system, health ex-
perts will take appropriate measures if older people
show abnormal signs in their activities or behavior.
We describe a hypothetical scenario of Alice, who
is 65 years old and suffers from pains in the knees
and wrist and a recent heart attack which his family
does not know. Alice relies on different body sensors,
which collect information by monitoring his motion,
Location, blood pressure, glucose level, ECG, and
pain levels. A nearby health center has a nurse, ”Ava”
who can also monitor all information using the system
shown in Figure 3. Ava can call the required doctor
if Alice’s condition deteriorates. He shared his auto-
generated medical reports with a group consisting of
doctors and family, but the last time he got a cardiac
attack, he shared the reports with doctors and not with
family. Also, Alice is concerned about his privacy and
wants to know which information is shared with oth-
ers and its purpose.
The following describes how the privacy require-
ments present in the hypothetical scenario can be
specified using our proposed framework.
Domain of Smart City: Smart Heath Care System.
Information Facet:Personal Information (Name,
age, profession, telephone, address and affiliation)
and Personal health data: Alice’s cardiology data.
Actor Facet: Alice, Ava, James
Relationship Facet: (Alice relationship with Ava:
Alice - Nurse), (Ava relationship with system: Ava
- system), (Alice’s relationship with James: Alice
Incorporating Privacy Requirements in Smart Communities for Older Adults: A Research Vision
Figure 3: Alice Data Monitoring using Smart Technologies.
James), (James relationship with the Institute: James
- Institution)
Trust Relationship: (Alice trust Ava that they will
not reveal his information to family), (Alice trust
James that they will not reveal his information to fam-
ily), (Alice trusts the James that he will share his in-
formation with other colleagues/doctors only when
needed), (James trusts his colleagues by allowing the
sharing of Alice health personal data).
Data Flow Facet: (Alice information flows to sys-
tem), (System sends notification to Ava), (Ava sends
information to doctor).
Owner, Consent and Permission: Alice is owner,
Granting permission to Ava to float his data to James
to doctor and if his condition deteriorates and may re-
quired any major procedure then Ava can inform his
Risk Dimension: Alice information is disclosed.
Vulnerability: Someone else access or shared Alice
Privacy Violation: (Intrusion may cause embarrass-
ment), (Disclosing Alice personal data may cause
worries problem at home).
Privacy Determinants: (Data leakage threats),
(Unauthorized access to system), (Human Mistakes)
Privacy Threats: Disclosing Alice information
caused threats i.e. Exclusion and Interference which
cause harms like Embarrassment
Privacy Goal: To make Alice data Confidential.
Privacy Constraint: (Alice should share data tem-
porarily), (James and Ava may not share the data
without permission of Alice).
Information Database: (Alice is user and group
members are Alice, James, Ava and family members
whereas reports are sharing object), (Reports not shar-
ing with family last time is conflict and history).
Alice’s privacy depends on Ava, James, and System.
If there is no undue access to his sensitive data, he
will not be embarrassed, nor will his family be wor-
ried. Figure 4 shows privacy is insured if there is no
unauthorized access to his confidential data.
Figure 4: Scenario Modelling.
This paper introduced a systematic approach to anal-
yse privacy requirements in smart cities from a re-
quirements engineering vie software flow by which
software analysts can take advantage. Also, we pro-
vided a framework to improve individual privacy in
social groups. We considered a hypothetical scenario
of older adults in the health care system for eliciting
privacy requirements. For now, our framework is yet
to be evaluated with real-life data, as we plan to con-
sider that for future work. Also, we aim to demon-
strate our proposed framework on the E-health sector
and get it evaluated by domain experts. Furthermore,
we intend to incorporate older adults’ emotional con-
cerns into the current proposed approach by leverag-
ing psychological theories and suggest a strategy for
managing conflicts in emotional goals based on our
earlier framework in (Gambo and Taveter, 2021) and
(Gambo and Taveter, 2022).
The authors would like to thank the Institute of Com-
puter Science at the University of Tartu, Estonia for
the support in executing the research. The research
has received funding from the European Social Fund
via the IT Academy programme awarded to the sec-
ond author.
Bhatia, J. and Breaux, T. D. (2017). A data purpose case
study of privacy policies. In 2017 IEEE 25th Inter-
national Requirements Engineering Conference (RE),
pages 394–399. IEEE.
Brandeis, L. and Warren, S. (1890). The right to privacy.
Harvard law review, 4(5):193–220.
ICSOFT 2022 - 17th International Conference on Software Technologies
Calikli, G., Law, M., Bandara, A. K., Russo, A., Dickens,
L., Price, B. A., Stuart, A., Levine, M., and Nuseibeh,
B. (2016). Privacy dynamics: Learning privacy norms
for social software. In 2016 IEEE/ACM 11th Interna-
tional Symposium on Software Engineering for Adap-
tive and Self-Managing Systems (SEAMS), pages 47–
56. IEEE.
Curumsing, M. K., Fernando, N., Abdelrazek, M., Vasa, R.,
Mouzakis, K., and Grundy, J. (2019). Understand-
ing the impact of emotions on software: A case study
in requirements gathering and evaluation. Journal of
Systems and Software, 147:215–229.
Doyle, T. (2011). Helen nissenbaum, privacy in context:
technology, policy, and the integrity of social life.
Dritsas, S., Gymnopoulos, L., Karyda, M., Balopoulos, T.,
Kokolakis, S., Lambrinoudakis, C., and Katsikas, S.
(2006). A knowledge-based approach to security re-
quirements for e-health applications. Electronic Jour-
nal for E-Commerce Tools and Applications, pages 1–
Friedewald, M., Finn, R. L., Wright, D., Gutwirth, S.,
Lenes, R., Hart, P., and Poullet, Y. (2013). Seven
types of privacy. European Data Protection: Coming
of Age, pages 3–32.
Gambo, I., Oluwagbemi, O., and Achimugu, P. (2011).
Lack of interoperable health information systems in
developing countries: an impact analysis. Journal of
Health Informatics in Developing Countries, 5(1).
Gambo, I., Soriyan, A., and Ikono, R. (2014). Framework
for enhancing requirements engineering processes: a
conceptual view of health information system. Inter-
national Journal of Computer Applications, 93(2).
Gambo, I. and Taveter, K. (2021). A pragmatic view on
resolving conflicts in goal-oriented requirements engi-
neering for socio-technical systems. In Proceedings of
the 16th International Conference on Software Tech-
nologies, ICSOFT 2021, pages 333–341.
Gambo, I. and Taveter, K. (2022). Stakeholder-Centric
Clustering Methods for Conflict Resolution in the Re-
quirements Engineering Process, volume 1556 CCIS
of Communications in Computer and Information Sci-
Gambo, I. P. and Soriyan, A. H. (2017). Ict implementa-
tion in the nigerian healthcare system. IT Professional,
Gharib, M., Giorgini, P., and Mylopoulos, J. (2021). Copri
v. 2—a core ontology for privacy requirements. Data
& Knowledge Engineering, 133:101888.
Harmon, R. R., Castro-Leon, E. G., and Bhide, S. (2015).
Smart cities and the internet of things. In 2015 Port-
land International Conference on Management of En-
gineering and Technology (PICMET), pages 485–494.
Ismagilova, E., Hughes, L., Rana, N. P., and Dwivedi, Y. K.
(2020). Security, privacy and risks within smart cities:
Literature review and development of a smart city in-
teraction framework. Information Systems Frontiers,
pages 1–22.
Langheinrich, M. (2002). A privacy awareness system for
ubiquitous computing environments. In international
conference on Ubiquitous Computing, pages 237–245.
e, A., P
ınez, P. A., and Solanas,
A. (2013). The pursuit of citizens’ privacy: a privacy-
aware smart city is possible. IEEE Communications
Magazine, 51(6):136–141.
McNeill, A., Briggs, P., Pywell, J., and Coventry, L. (2017).
Functional privacy concerns of older adults about per-
vasive health-monitoring systems. In Proceedings of
the 10th international conference on pervasive tech-
nologies related to assistive environments, pages 96–
Miller, T., Pedell, S., Sterling, L., Vetere, F., and Howard,
S. (2012). Understanding socially oriented roles and
goals through motivational modelling. Journal of Sys-
tems and Software, 85(9):2160–2170.
Omoronyia, I., Cavallaro, L., Salehie, M., Pasquale, L., and
Nuseibeh, B. (2013). Engineering adaptive privacy:
on the role of privacy awareness requirements. In
2013 35th International Conference on Software En-
gineering (ICSE), pages 632–641. IEEE.
Peters, F., Hanvey, S., Veluru, S., Mady, A. E.-d.,
Boubekeur, M., and Nuseibeh, B. (2018). Generat-
ing privacy zones in smart cities. In 2018 IEEE Inter-
national Smart Cities Conference (ISC2), pages 1–8.
Runeson, P., H
ost, M., Rainer, A., and Regnell, B.
(2012). Case study research in software engineering-
guidelines and examples wiley.
Schaub, F., K
onings, B., Weber, M., and Kargl, F. (2012).
Towards context adaptive privacy decisions in ubiq-
uitous computing. In 2012 IEEE International Con-
ference on Pervasive Computing and Communications
Workshops, pages 407–410. IEEE.
Solove, D. J. (2005). A taxonomy of privacy. U. Pa. L. Rev.,
Taveter, K., Sterling, L., Pedell, S., Burrows, R., and
Taveter, E. M. (2019). A method for eliciting and
representing emotional requirements: Two case stud-
ies in e-healthcare. In 2019 IEEE 27th Interna-
tional Requirements Engineering Conference Work-
shops (REW), pages 100–105.
Thielke, S., Harniss, M., Thompson, H., Patel, S., Demiris,
G., and Johnson, K. (2012). Maslow’s hierarchy
of human needs and the adoption of health-related
technologies for older adults. Ageing international,
Thomas, K., Bandara, A. K., Price, B. A., and Nuseibeh,
B. (2014). Distilling privacy requirements for mobile
applications. In Proceedings of the 36th international
conference on software engineering, pages 871–882.
Van Zoonen, L. (2016). Privacy concerns in smart cities.
Government Information Quarterly, 33(3):472–480.
Westin, A. F. (1968). Privacy and freedom. Washington and
Lee Law Review, 25(1):166.
Incorporating Privacy Requirements in Smart Communities for Older Adults: A Research Vision