Model-driven Approach for Privacy Management in Business
Christophe Feltus
, Eric Grandry
, Thomas Kupper
and Jean-Noël Colin
Luxembourg Institute of Science and Technology (LIST), IT for Innovative Services (ITIS),
Esch-sur-Alzette, Luxembourg
Faculty of Computer Science (PReCISE), University of Namur, Namur, Belgium
Keywords: Privacy Metamodel, Privacy Management, GDPR, General Data Protection Regulation, Business Ecosystem,
Interconnected Society, Model, Service System, Model-driven Approach, Model Design.
Abstract: Protection of individuals with regard to the processing of personal data and the free movement of such data
constitutes new challenges in terms of privacy management. Although this privacy management ought to be
conducted in compliance with national and international regulation, for now we observe that no solution,
model or method, fully consider and integrate these new regulations yet. Therefore, in this paper, we propose
to tackle this problem through the definition of an expressive privacy metamodel which aims to represent and
aggregate the concepts that are relevant to define and to deal with privacy issues, at an organizational level.
Secondly, we discuss how this privacy metamodel may support and may help understanding the management
of the privacy in enterprises involve in interconnected societies, by integrating the privacy metamodel with
the systemic business ecosystem.
Among the many challenges related to the privacy
management is the protection of individuals with
regard to the processing of personal data and the free
movement of such data. This privacy management
should be conducted by considering the arising
dedicated national and international regulation and
more especially, in Europe, the General Data
Protection Regulation (GDPR) that is puts forward by
the Regulation of the European Parliament and of the
European Council. At the business level, the Ipswitch
survey (Ipswitch, 2015) on 316 European companies
reveals that 52 percent of the respondents estimate
that they are not ready for applying the GDPR, and
that 56 percent did not even know exactly what
GDPR is. Only 12 percent feel ready to be compliant
with it. A reason for this is that, at the present day, no
solution (i.e. model, method, or tool) fully considers
and integrates this new regulation yet. Simple and
adapted approaches are by the way required to
support information system designers to apprehend
the GDPR and its impact on the whole business
organization and business ecosystem. In that regard,
we propose, in this paper, to set forth preliminary
theoretic researches to define an expressive privacy
metamodel (PMM) which allows representing and
aggregating in an extended metamodel all the
concepts necessary to define and to deal with privacy
issues, at an organizational level. This metamodel is
designed in compliance with Service System
Theories (SST, Alter, 2011) and in the frame of a
design (Hevner et al., 2004, Peffers et al., 2008)
science approaches, including iterative cycles of
literature review/model design/validation.
Afterwards, to analyse and to depict the privacy
management in the frame of interconnected societies
(Cholez et al, 2014), we integrate the PMM with the
business service ecosystem model and we discuss
how the comprehension of the systemic privacy
management may be improved on the basis of a
model-driven approach. Finally, we illustrate the
advantage of this integration into a case study from
the Luxembourgish financial sector.
At a methodological level, the research that we
tackle through this paper concerns the improvement
of the alignment between the information processed
by the information system and the management of the
privacy, as required by the literature and by specific
legal requirements, such as, mainly the GDPR.
Through this research, we aim to strengthen the
organizational capability to manage the access to
Feltus C., Grandry E., Kupper T. and Colin J.
Model-driven Approach for Privacy Management in Business Ecosystem.
DOI: 10.5220/0006142203920400
In Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2017), pages 392-400
ISBN: 978-989-758-210-3
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
sensitive and private information by enhancing the
latter’s ability to apprehend privacy and to comply
with privacy requirements. At the methodological
level, Hevner et al. (2004) explains that the Design
Science Research (DSR) paradigm seeks to extend
the boundaries of human and organization capability
by creating new and innovative artefacts. Practically,
provided that we aim to design a new artefact to
support the alignment between private information
and the management of the latter with the objective to
grant the appropriate access rights to the users of this
information, we acknowledge that the research may
plainly be considered in the scope of DSR, as
expressed in Peffers et al. (2008). As advocated by
the DSR theory, the metamodel is built following an
iterative approach. The latter allows refining a
consistent privacy metamodel based on the review of
the scientific literature and on the analysis of the
privacy regulation.
The paper first proposes a review of the literature
related to the privacy and depicts the related
regulation Section 3 explains the design of the PMM
and integrates it with systemic approach. Section 5
presents a case study from the financial sector.
Finally, Section 6 discusses how it contributes in
sustaining systemic privacy management, and
Section 7 concludes the paper and proposes some
future works.
This section reviews the scientific literature related to
privacy and the legal requirement from the GDPR.
2.1 Scientific Contribution
As explained in De Capitani di Vimercati et al.
(2012), several definitions of privacy have been
proposed over the years, from traditional syntactic
privacy definitions to more recent semantic privacy
definitions. In this paper, we consider privacy as
sensitive information that is individually owned
(Nuseibeh, 2010). The privacy management includes
many aspects like the definition of privacy policy, the
expression of the policy with a dedicated language or
the execution of the latter with dedicated
mechanisms. At a modelling point of view, privacy
has often been addressed in parallel and as an
extension of the access rights models (Park et al.,
2000, Ni et al., 2007). For instance, Ardagna et al.
(2008) analysed the concepts and features that should
be investigated to fulfil the development of powerful
and flexible privacy-aware models and languages.
Privacy has been considered through the lens of the
purpose associated to the usage of personal
information. In that regards, a first model proposed
by Antón et al. (2007) was UCON (Usage Control)
which gathered in a single model the traditional
access control models, the trust management and the
digital rights management. In contrast to traditional
access control models that control the access to an
information in a unique direction (for instance, from
the eCommerce site to the customer), UCON allows
controlling the access at both sides and, hence, allows
a user to control the information provided to the
eCommerce website, which as a result guarantees the
privacy to this information.
Park et al. (2002) explains that individual policies
for each user’s activity and for the use of each
resource are similar to subject and object attributes
and that the mutability of these attributes allows the
continuity of decision (e.g. if a usage is no longer
necessary, the access right is removed). In Martinez-
Balleste et al. (2013), a framework is proposed to
exploit the notion of privacy awareness requirements
in order to detect runtime privacy properties to be
satisfied. The latter are exploited to support
disclosure decisions made by the applications. In Park
et al. (2000), the Role Based Access Control (RBAC)
model has been extended to provide support for
expressing privacy policies (PRBAC) by considering
at the same time users’ purposes and obligations.
Providing the system to protect customer privacy in
mobile applications is a challenging task (Mahmoud
et al., 2005). In Domingo-Ferrer et al. (2007), the
author expresses that new development methods are
necessary to enable the engineering of privacy
specific requirements and proposes to extend the
PRBAC model to reason about scenarios that
potentially exploit mobile systems weaknesses
(Martinez-Balleste et al., 2013). More recently,
OrBAC has been semantically enriched to model
privacy policies. The enhancement consists in
considering the concepts of consent, accuracy,
purposes of the access and provisional obligation (Ni
et al., 2007). Ajam et al. (2013) analyse how OrBAC
and PRBAC are adapted to address security issues in
the healthcare sector and observes, amongst others,
(1) that OrBAC is not adapted for managing the
privacy regarding some roles (like the legal
representative and the trusted person) given that a
user must be strictly attached to an organization, and
(2) that PRBAC fits well with the requirements of the
information system from the healthcare regarding the
patient’s records (Ajam et al., 2013).
Rath et al. (2012) addresses privacy through the
purpose of the access and proposes a model for
Model-driven Approach for Privacy Management in Business Ecosystem
purpose enforcement. Accordingly, the authors also
propose a system architecture that contributes to the
enforcement of access purpose. In the field of privacy
in social network circles, Rath et al. (2013) observe
that existing privacy approaches are still not able to
deal with the user’s changing information sharing
privacy requirements. This motivates the design of a
utility-based trade-off framework which models and
quantifies users’ requirements and, based on it,
appraises the potential privacy risks, on one hand, and
the incentive social benefit, on the other. Regarding
the organization more closely, Merriam (2016) has
proposed a framework to address the database privacy
according to three independent privacy dimensions:
the respondent privacy (the entity to which the data
collected corresponds), the user privacy (the entity
that uses the data or makes queries in the database),
and the owner privacy (the entity that owns the data).
Using this framework, Merriam (2016) assesses the
existing privacy enabling technology and observes
that none of them allow fulfilling the three privacy
dimensions at the same time.
The risk in revealing a user identity via location
information has been formally introduced by Bettini
et al. (2005) who present preliminary ideas about
algorithms to prevent this to happen. More recently,
Yang et al. (2014) propose a W3-privacy method to
address the Location-Based Services (LBS – Service
required by mobile devices with self-location
capabilities). This method concerns the user privacy,
according to the model from Merriam (2016), and is
based on density maps (where the user identification
is made uncertain due to the great number of users).
Three elements of a service request are considered by
W3-privacy: Where the request is done?, What is
requested?, and Who makes the request?. Using the
database privacy framework [4] and the LBS privacy
method from Yang et al. (2014), Pérez-Martinez et al.
(2011) propose a 5D citizens’ privacy model for
smart cities. The five privacy dimensions concerned
by the model are the identity privacy (identity of the
user of a service), the query privacy (query made by
the user), the location privacy (the place where the
query is done), the footprint privacy (information
retrieved from sensors), and the owner privacy
(equivalent to the definition of Merriam (2016).
2.2 GDPR
The General Data Protection Regulation (GDPR) is a
Regulation which has for objective to strengthen and
to unify, within the European Union, personal data
protection and the export of such data outside the EU
borders. The GDPR aims to replace the data
protection directive 95/46/EC (DP, 1995) from 1995.
Based on the deployment of the GDPR, it is expected
to pass back the control on the usage of their personal
data to European citizens. According to the European
Commission, personal data is “any information
relating to an individual, whether it relates to his or
her private, professional or public life. It can be
anything from a name, a photo, an email address,
bank details, posts on social networking websites,
medical information, or a computer’s IP address".
The GDPR lays down a set of rules with regard to
the processing and related to the free movement of
personal data. Hence, GDPR is to be read more as a
regulation for the management of the privacy than for
its definition. Accordingly, GDPR is more related to
the concepts of privacy management (in light grey in
Figure 5). The review of the articles of the GDPR
which are relevant for the definition of the PMM has
allowed capturing the following set of requirements
necessary to be considered, in Table 1.
Table 1: GDPR principles and requirements about personal
Art. Description
Processed lawfully, fairly and in a transparent
manner in relation to the data subject
Collected for specified, explicit and legitimate
Adequate, relevant and limited to the minimum
5d Inaccurate must be erased or rectified
Data subject to be identifiable for no longer
than necessary
Controller must ensure the compliance with the
Data subject gives consent to the processing
and has the right to withdraw his/her consent
Data processing is necessary for performance of
a contract, legal obligation, vital or public
interest, legitimate interests pursued by a
The privacy metamodel (1) is structured according to
three of the core concepts that compose the SST
(Alter, 2011), i.e., the Resource, the Role, and the
Activity and (2), based on the review of the state of
the art summarized in Section 2, the metamodel is
extended to the Privacy management. Concretely,
during the elaboration of this first iteration of the
metamodel, each relevant element from the review of
MODELSWARD 2017 - 5th International Conference on Model-Driven Engineering and Software Development
the state of the art (scientific contributions and
GDPR) is introduced in the metamodel. In order to
give an exhaustive view on the latter, and to allow
traceability during the design step, a reference to the
source of the relevant argument is introduced directly
in the model, aside the concept’s class name or the
relation name between concepts, in brackets.
Concerning the GDPR, for accuracy reasons, a
reference is directly done to the specific article which
provides the requirement. E.g. the reference (GDPR
A6.1b) is to be read: Article 6, point 1, sub-item (b).
It is worth to mention that not all the components of
the privacy are represented in this version of the
metamodel. Some concepts and some relations have
been omitted for preserving the readability. This
metamodel has been modelled in UML 2.0
(Rumbaugh et al., 2004) and cardinalities have been
3.1 Resource Related Concepts
The resource domain (Figure 1) represents the set of
elements which are used by activities (Alter, 2011),
e.g., participant, technology or information. The
privacy of the information relates, as a result, to the
privacy of a type of resources. This information
supports the representation of knowledge, what
signifies understanding of real things or abstract
concepts (OPL). It may be of a sensitive type when it
concerns personal data (Nuseibeh, 2010) such as a
picture, a mail address, a physical characteristic of an
individual, etc. In turn, a subset of this sensitive
information may be an information which is relevant
for a specific business and in that regard, limited to
the minimum necessary to be collected and exploited
by the information system (Zhu et al., 2007, GDPR
The concept of sensitive information is introduced
and represented as a class in the privacy metamodel.
It is of the type “resource” on Figure 1.
Figure 1: Resource sub-model concepts.
3.2 Role Related Concepts
The role domain gathers a set of roles having
responsibilities in the management of the privacy, i.e.,
the information owner, the controller, the information
user. The information owner is the actor who is
legally accountable for the protection of the
information collected. Hence, it is his responsibility
to decide when and how information may be released
or made available for processing. He is also called the
data collector (Merriam, 2016) and corresponds to the
individual or institution with which there exists a trust
relationship with the information respondent. It may
correspond to a person or to an institution, like for
instance, a hospital, a bank, or a book-keeper. The
controller must guarantee transparent and easily
accessible information on the protection of personal
data and privacy as well as the procedures for the data
subject to fulfil its rights (GDPR). The controller
must keep the information owner informed about the
data manipulation (GDPR A14). The information
user corresponds to an entity being able to compute
queries across the databases in such a way that only
the results of the query are revealed (Merriam, 2016).
The information user corresponds to the “processor"
in the GDPR. Moreover, Art. 26 of the GDPR stresses
the fact that in case of a processor that processes data
upon request of the controller, the processor must be
associated to a joint controller.
This statement has not been modelled in the
privacy metamodel for the sake of clarity. According
to the GDPR, both the controller and the information
user are responsible to set up the appropriate
protection of the data. Moreover, roles are played by
actors. A specific one of them is the data subject.
The data subject (GDPR) is the entity to which the
information records correspond (Merriam, 2016). It is
for instance the patient in a hospital or the customer
of a bank. The data subject is allowed to give or
withdraw his consent on the usage of personal
information (GDPR A6.1a) and this usage must be
transparent for him (GDPR A5a). Finally, Yang et al.
(2014) also claims that the location where the actors
Figure 2: Role sub-model concepts.
Model-driven Approach for Privacy Management in Business Ecosystem
operate is also a privacy parameter to be considered.
At the modelling level (figure 2), we have
represented the concept of Role and Actor as classes.
The first is played by the second. The concepts of
information owner, controller, and information user
are specializations of the class role, and the data
subject is a specialization of the class actor.
3.3 Activity Related Concepts
In the frame of the Service System Theories (Alter,
2011), the activity is an element contains by the
organization that produces services using resources.
The privacy management is a type of activity which
aims to support the information owner in performing
the activities related to the administration of private
data. The privacy management domain consists
mainly in three activities: the definition of the privacy
policy, the enforcement of the policy, and the audit of
the policy (Ashley et al., 2003). The management of
the privacy is performed in compliance with the
privacy policy. This policy provides requirements in
the way of how the privacy of personal data must be
preserved and managed, in a company, while
remaining in turn compliant with the appropriate
legislation. This privacy policy determines the
minimum needed and relevant information (Zhu et
al., 2007, GDPR A5c) necessary to achieve a task and
is applicable at all stages of the privacy management,
to know: the collecting, the usage, the update, the
disclosure and the erasure of the information. The
three most important activities of the privacy
management, according to Ashley et al. (2003), are
the specification of privacy policies, their
enforcement and the audit of their deployment.
The specification of the privacy policy aims at
establishing the general and specific goals, as well as
the procedures necessary to fulfil these goals. These
privacy policies are adopted by the controller (GDPR
A5f). The activity related to the enforcement of the
policies corresponds to the achievement of
procedures for the privacy management, either
manually or with the support of the information
system. The access rights, to the information system,
represent hence a huge part of the means necessary
for granting, or not, access to the information [8], and
more especially, the minimum necessary and relevant
information (Zhu et al., 2007, GDPR A5c). In that
regard, the audit of the privacy policy concerns both:
(1) that access rights/authorization are/is deployed in
compliance with the privacy policy (Antón et al.,
2007) and (2) that the latter is provided in accordance
with the usage of the private data (Antón et al., 2007).
At a modelling point of view, the privacy policy
and the privacy management concepts have been
introduced as classes in the metamodel. The latter is
a type of activity in the SST, it supports the
information owner in fulfilling its responsibilities and
is composed of the three explained activities here
above, i.e. the definition of the privacy policy, the
enforcement of the policy, and the audit of the policy.
Figure 3: Activity sub-model concepts.
These concepts are also modelled as classes and
the latter compose the privacy management class
(actors and activity domains are represented in light
grey in Figure 5).
3.4 Privacy Related Concepts
The Privacy extension domain supports the definition
of privacy in function of the usage (Antón et al.,
2007) made with the information and in function of
the actors who manipulate the latter (Yang et al.,
2014, Pérez-Martínez et al., 2011). As reviewed in
literature and more especially in the GDPR, the usage
concerns the information which is relevant for the
business, to which access rights must be restricted to
the minimum necessary (Zhu et al., 2007, GDPR
A5c), and for which a purpose is clearly defined
(Antón et al., 2007, GDPR A5b, A6.1b to d).
This purpose must be included in a list of well-
defined purposes, expressed in the GDPR, e.g., for the
performance of a contract of which the information
respondent is part, for compliance with some legal
obligations, in order to protect the vital interests of the
information respondent, to carry out a task of public
interest or in exercise of an official authority, or for a
legitimate interest pursued by the controller. At a
modelling point of view, the class privacy and the
MODELSWARD 2017 - 5th International Conference on Model-Driven Engineering and Software Development
Figure 4: Privacy sub-model concepts.
relation class usage, that influences the need of
privacy, have been modelled. In order to model this
usage depending on the purpose, in the metamodel,
we consider that this purpose is a type of attribute of
the usage class (Figure 4).
3.5 Integrated Privacy Metamodel
Figure 5 presents the integrated PMM gathering the
concepts from the resources, role, activity and privacy
sub-domains. This integration allows establishing
hyphens between concepts from all sub-domains,
amongst which, e.g. the tasks and the actors/roles.
The most important one is that the activity of a type
Privacy management main objective is to support
(Zhu et al., 2007) the information owner which has to
declare the (usage) intention (Zhu et al., 2007) related
to the sensitive information and to keep the latter up
to date (GDPR A5.5e) (to know: erase or rectify).
Aside the information owner, Controller defines
(GDPR A5f) and audits (Antón et al., 2007) privacy
policies, and Information user exploits resources in
compliance with access rights and authorizations.
This section give insights into the management of
privacy at systemic level based on a model driven
approach. Business ecosystems gather enterprises
which collaborate to achieve a common systemic goal
like guaranteeing national healthcare (Feltus et al.,
2014), telecommunication (
Wang et al., 1998), or
financial stability (Naudet et al., 2016). A metamodel,
named Business Service Ecosystem (BSE), was
proposed (Feltus et al., 2016) to model these business
ecosystems based on capabilities and resources
(Figure 6). The BSE purpose is to represent how the
resources of the business ecosystem are derived from
the business ecosystem enterprises capability. We
observe that two mappings exist between the PMM
and the BSE. First, the resource from the BSE, at the
business ecosystem or at the enterprise level, which is
defined as an asset that an organization has or can
call upon, is mapped with the resource from the
privacy metamodel that refers to the SST (Alter,
Figure 5: PMM. White concepts relates to resource, light grey to role and activity, and dark grey to the privacy extension.
Model-driven Approach for Privacy Management in Business Ecosystem
2011) and which is defined as participants,
technological entities, informational entities, and
other resources used by activities. Second, there is an
exact equivalence between the service definition from
PMM (SST definition) and the service definition from
BSE: “acts performed for others, including the
provision of resources that others will use”.
Figure 6: BSE metamodel, extracted from [28]. Pattern A
corresponds to the business ecosystem and B to the
At the systemic level, we have demonstrated in [28]
that (1) an enterprise service (pattern B) is a type of
business ecosystem resource (pattern A) and (2) the
capability, which is defined as “the ability and
capacity that enable an enterprise to achieve a
business goal in a certain context” [26] is necessary
to realize an enterprise/systemic service.
In this paper, we have elaborated a PMM from a
review of scientific privacy literature and from
requirements from the GDPR. This metamodel is
structured following the SST and allows structuring
the management of the privacy at the enterprise level
by defining privacy management services, supported
by dedicated capabilities and specific roles.
Afterwards, the privacy metamodel has been
integrated with the BSE to extend the usage of the
PMM to the context of enterprise working in the
frame of interconnected societies.
Many approaches have been proposed to tackles
privacy issues at the enterprise level (Section 2).
However, as far as our knowledge goes, none of them
has ever proposed an integrated metamodel for
representing the many dimensions of the privacy,
especially in compliance with the GDPR. In that
regard, the first part of the paper has proposed the
privacy metamodel built on existing theories and
legal requirements. Aside our developments, we have
also observed that in an interconnected societies and
sharing economy context, managing the privacy at the
enterprise level is far to be sufficient, and that new
approaches are required to raise the privacy
management from the enterprise boundaries up to the
business ecosystem level. To answer this arising
business requirement, we have proposed in Section
4.2 to integrate the PMM with the business service
ecosystem. Afterwards, to assess the relevance and
accuracy of the privacy metamodel in interconnected
societies, we have evaluated the deployment of this
artefact in the frame of substantive instances through
a real financial case study. The latter has
demonstrated that if we consider that the PMM
structures the requirements for a compliant
management of the privacy at the enterprise layer, it
is also relevant to instantiate the concepts of this
metamodel in order to apprehend and thus to manage
privacy issues at the business ecosystem level. Based
on the mapping with the BSE, we observe first that in
order to manage the privacy (which may be perceived
as a new type of service), the enterprise is required to
develop new capabilities, respectively: to define
privacy policy, to enforce the latter and to assess their
enforcement. These capabilities also requires specific
resources, e.g., some employees need to be affected
to new privacy management roles like the information
user, the controller or the information owner. New
tools also need to be acquired to support the
performance of Privacy Impact Assessment and
access rights management. At the business ecosystem
level, our analysis reveals that the same instantiation
is necessary, although it sounds to be much trickier.
Indeed, in this case, privacy management needs to be
handled by a business ecosystem authority like, for
instance in the case of the CSSF. The latter needs to
analyse the systemic capabilities and resources
required to realize systemic privacy services
(business ecosystem service) in a service system
environment. This context, justifies the need for the
model-driven approach that we propose and which
sustain the abstraction of the information system at
the right level to support systemic privacy
management. In regard to this evolution, we also
observe the arising of new type of specific businesses
to manage the systemic privacy (other type of
business ecosystem services). These arising services
in turn necessitate business ecosystem capabilities
and resources such as dedicated systemic privacy
management role, tools, and policies. A particular
resource in this context concerns the Privacy Impact
MODELSWARD 2017 - 5th International Conference on Model-Driven Engineering and Software Development
Assessment methods. Various initiatives emerges to
extend the use of risk assessment to the privacy
domain. E.g., the Commission nationale de
l'informatique et des libertés (CNIL - In English:
National Commission on Informatics and Liberty)
proposes a privacy risks assessment method, which
can be integrated in a privacy impact assessment
(Netha, 2016). The National Institute of Standards
and Technology (NIST) is also developing a specific
privacy risk management model and framework and
attempting to integrate it with its security risk
management framework. These initiatives can be
seen as an extension to the Australian National
eHealth Security and Access Framework approach
(Netha, 2016), as they do not only address the CIA
triad, but additional objectives associated with
privacy. As the privacy objectives of the individuals
are translated into objectives of the organization, they
however remain focalized on fully assessing the risks
(the combination of both threat and impact) on the
organization. The PMM, as an extension of the SST,
is an artefact that could sustain the PIA management.
Therefore, the preliminary work related to the
mapping between the risk and the BSE could serve as
a good basis considering that risk of enterprise
privacy breach is a function of the tuple privacy
threat, privacy impact, privacy vulnerability. Based
on the integration of BSE - PMM, and given the
alignment between the risk concepts and the BSE, it
is possible to extrapolate the enterprise PIA to the
sectorial level as well.
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