How to Improve the GDPR Compliance through Consent Management
and Access Control
Said Daoudagh
1,2 a
, Eda Marchetti
1 b
, Vincenzo Savarino
3 c
, Roberto Di Bernardo
3 d
and Marco Alessi
3 e
ISTI-CNR, Pisa, Italy
University of Pisa, Pisa, Italy
Engineering Ingegneria Informatica, Italy
Access Control, Consent Management, GDPR, Privacy-by-Design.
This paper presents a privacy-by-design solution based on Consent Manager (CM) and Access Control (AC)
to aid organizations to comply with the GDPR. The idea is to start from the GDPR’s text, transform it into a
machine-readable format through a given CM, and then convert the obtained outcome to a set of enforceable
Access Control Policies (ACPs). As a result, we have defined a layered architecture that makes any given sys-
tem privacy-aware, i.e., systems that are compliant by-design with the GDPR. Furthermore, we have provided
a proof-of-concept by integrating a Consent Manager coming from an industrial context and an AC Manager
coming from academia.
The General Data Protection Regulation (GDPR) is
the EU Data Protection Regulation (European Union,
2016) in charge of harmonizing the regulation of Data
Protection across the EU member states. At the same
time, it enhances and arises business opportunities
within the Digital Single Market (DSM) space. How-
ever, the natural language nature of the GDPR makes
most of the provisions to be expressed in generic
terms and does not provide specific indication on how
they should be actuated. As a consequence, assuring
the GDPR compliance, and therefore avoid the related
fines, becomes an important research challenge.
Currently, many businesses are struggling in the
definition of appropriate procedures and technical so-
lutions for their development process so as to enforce
and demonstrate the GDPR compliance (Krenn S. et
al., 2020). More precisely, they recognized as a piv-
otal factor in the availability of automated supports
for specifying privacy requirements, controlling per-
sonal data, and processing them in compliance with
the GDPR.
From a practical point of view, scientific com-
munities, private companies, and European projects
such as CyberSec4Europe (Cyber Security Network
of Competence Centres for Europe)
are identifying
in the consent and security services, the successful el-
ements for automatic specification and enforcing the
data protection regulation (Krenn S. et al., 2020).
Indeed, the consent services may allow citizens
and companies to manage and track personal data in a
straightforward, user-centric, and user-friendly man-
ner; while the security services, and specifically the
authorization systems (i.e., Access Control (AC)), can
enforce the data protection regulations taking into ac-
count additional legal requirements, such as the data
usage purpose, user consent, and the data retention
period. Therefore, the joint work of the consent
and security services may overcome the challenging
and error-prone task of extracting legal and machine-
readable policies directly from the GDPR’s rules.
Currently, different research activities have been
devoted to define and implement privacy knowledge
and rules (Sforzin A. et al., 2020), but no generic so-
lution is still available. Along these lines, under the
Daoudagh, S., Marchetti, E., Savarino, V., Di Bernardo, R. and Alessi, M.
How to Improve the GDPR Compliance through Consent Management and Access Control.
DOI: 10.5220/0010260205340541
In Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021), pages 534-541
ISBN: 978-989-758-491-6
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
hypothesis that the joint integration of access control
systems and consent manager can enhance the con-
troller’s and processor’s compliance with the regula-
tion, this paper wants to provide the basic architecture
of a generic and practical solution to solve the GDPR
compliance problem.
In presenting our idea, we focus on the following
primary Research Question (RQ):
How a consent manager solution can be im-
proved with access control for assuring com-
pliance with the data protection or privacy
In answering the above RQ, we present a possi-
ble privacy-by-design architecture by integrating AC
and consent management systems. Finally, an imple-
mentation of the proposed architecture by using real
available solutions for the consent management and
the Access Control Mechanism (ACM), coming from
both industry and academia, is presented.
Outline. Section 2 presents the basic concepts used
along the proposal and related works; Section 3
describes the proposed solution by answering our
RQ; Section 4 shows the proof-of-concept we imple-
mented by instantiating the proposed solution with
real artifacts coming from both industrial and aca-
demic contexts; and finally, Section 5 concludes the
paper and illustrates future works.
This section introduces the main concepts used along
the present work: the GDPR concepts, Access Con-
trol, and Smart ICT Systems; and reports the related
The GDPR Concepts. The General Data Protec-
tion Regulation (GDPR) (European Union, 2016) de-
fines Personal Data as any information related to an
identified or identifiable natural person called Data
Subject. That means that, a data subject is a Natural
Person (a living human being), whose data are man-
aged by a Controller. The GDPR aims to ensure equal
protection of the Human Rights of the European Cit-
izens, to eliminate the barriers for the services to be
delivered in the European Union, and to enhance busi-
ness opportunities within the Digital Single Market
The GDPR is applied to the processing of per-
sonal data, whether it is automated (even partially) or
not. It defines, among others, the following princi-
ples and demands: Purposes, i.e., data should only
be collected for determined, explicit and legitimate
purposes, and should not be processed later for other
purposes; Accuracy, i.e., the processed data must
be accurate and up-to-date regularly; Retention, i.e.,
data must be deleted after a limited period; Subject
explicit consent, i.e., data may be collected and pro-
cessed only if the data subject has given his/her ex-
plicit consent.
Access Control. The eXtensible Access Control
Markup Language (XACML) (OASIS, 2013) is one
of the most widely used AC languages. It provides a
reference architecture including specific components
for managing policies and access requests. Indeed,
the evaluation of the the policy against the request
provides response corresponding to the authorization
decision. Very briefly, an XACML policy is a spe-
cific statement of what is and is not allowed on the
basis of a set of rules, defined in terms of conditions
on attributes of subjects, resources, actions, and envi-
ronment, and combining algorithms for establish the
precedence among the rules.
In this paper, Access Control mechanism becomes
a means for restricting access to personal data, based
on the GDPR compliant Access Control Policies
(ACPs), i.e., a set of rules that specify who (e.g.,
Controller, Processor or Data Subject) has access
to which resources (e.g., Personal Data) and under
which circumstances (i.e., the GDPR’s demands, such
as Purpose and Consent).
Smart ICT System. Smart ICT Systems (or Ser-
vices) are becoming increasingly important in almost
all industries and areas of today’s society (Neuh
et al., 2020). They rely on the integration and im-
plementation of innovative tools and techniques that
make a given system smart to strengthen economic
needs (Samir Labib et al., 2018). Despite their in-
creasing significance, a distinct definition of Smart
ICT has not yet evolved in the scientific literature.
Nevertheless, it is possible to identify a very high-
level abstract architecture for a standard Smart ICT
System, as depicted in Figure 1. Commonly, it is
composed of a Smart ICT Core System that offers
the main functionalities to Smart Services in terms of
both hardware and smart software (e.g., Cloud Com-
puting, Internet of Things, and Big Data). Conse-
quently, developers use these functionalities to con-
ceive and implement Smart Services that end-users
consume to achieve a given business or personal
How to Improve the GDPR Compliance through Consent Management and Access Control
Figure 1: A Smart ICT System.
Smart ICT Core System is also in charge of man-
aging the resource and data access by using either cus-
tomized facilities or by relying on a specific Access
Control System. To this purpose, in Figure 1 an Ac-
cess Control Policies repository has been considered.
Related Work. Over the last years, different solu-
tions have been proposed for the enforcement of the
GDPR compliance into the Smart ICT Systems. They
can be roughly divided into the following categories:
Solutions applicable at Business Processes level,
i.e., mainly focused on the behavioral aspect (Bar-
tolini et al., 2019a; Calabr
o et al., 2019;
and Große, 2020; Sokolovska and Kocarev, 2018).
Proposals providing supporting facilities for
transforming the GDPR’s text into executable ac-
cess control policies. In this case, the policies
are either systematically derived from the GDPR,
e.g., (Bartolini et al., 2019c; Dernaika et al., 2020)
or generated through intermediate formal struc-
tures (Bartolini et al., 2019b; Carauta Ribeiro and
Dias Canedo, 2020).
Proposals easily enforceable into the Smart ICT
Systems architecture. They can be roughly clas-
sified into: i) those using access control mecha-
nisms for the protection of personal data within
Smart ICT Systems perimeters (Greaves et al.,
2018); ii) those using Smart ICT Systems users
location information for authenticate the customer
and manage his/her data (Haofeng and Xiaorui,
2019); and iii) those exploiting specific secu-
rity attributes for assuring the GDPR compli-
ance (Jensen et al., 2013; Barsocchi et al., 2018;
o et al., 2019).
Our answer to the RQ wants to merge the best
practices of the identified above research areas. In-
deed, we are proposing the integration of the con-
sent and access control management for enhancing
the business process execution with specific activi-
ties able to modeling and enforcing the GDPR legal
In this section, we answer the RQ presented in the
introduction of this paper by integrating consent and
access control management for assuring compliance
with a reference data protection legal framework, i.e.,
the GDPR.
In the remainder of this section, details about the
proposed reference architecture are provided. Indeed,
they are our positive answer to the RQ. Additionally,
to remark the feasibility of the proposed solution, we
also provide its possible instantiation by using two
real-world systems coming from both industrial and
academic contexts. Details of this integration are re-
ported in Section 4.
3.1 A Privacy-by-Design Smart ICT
In this section, we describe the Privacy-By-Design
Smart ICT System layer, that provides features for in-
teracting directly with smart services and end-users of
the system to guaranteeing compliance with the EU
Figure 2: A Privacy-By-Design Smart ICT System Pro-
By referring to Figure 1, the extended architec-
ture is schematized in Figure 2, where the Privacy-
By-Design Smart ICT System is represented by the
external grey square. As in the figure, this layer
has the responsibility to interact with end-users (in
our case Data Subject and the Smart Services) of the
Smart ICT system. It is also in charge of managing
all the domain dependent activities that are necessary
for the end-users interactions. More precisely, the
Privacy-By-Design Smart ICT System includes: (1)
the components already part of the Smart ICT core
systems (described in Section 2) and represented as
blue square labeled Smart ICT Core System; and (2) a
new layer called GDPR Manager, that is in charge of
the translation and enforcement of executable access
ICISSP 2021 - 7th International Conference on Information Systems Security and Privacy
control policies. This is represented by the orange
square labeled GDPR Manager, and detailed more in
the remainder of this section.
3.2 GDPR Manager
The GDPR Manager includes two main components
(see Figure 2): Consent Manager that translates the
textual consent into structured representation, and Ac-
cess Control Manager that provides enforceable ac-
cess control policies.
Consent Manager. The aim of Consent Manager is
to manage and control personal data during the in-
teraction among Data Subjects and public and private
services as Data Controller and Processors (e.g., PA,
Social, IoT, B2C). It provides facilities for lawful data
sharing processes, with the ability to grant and with-
draw consent to third parties for accessing own per-
sonal data. Concerning Smart Services, the Consent
Manager should allow them to define specific pur-
poses for each operation (i.e., processing activities)
and the data needed to accomplish the required tasks
lawfully. Thus, the Consent Manager should include a
consent-based, user-centric interface enabling: (1) the
data subjects to manage, trace their own data and its
associated consent; (2) the data controllers/processors
to use consent to data sharing among digital services
using personal data and meet the GDPR’s require-
ments. Additionally, Consent Manager should guar-
antee by-design the compliance with the GDPR’s de-
mands, such as data minimization and purpose limi-
tation principles.
Access Control Manager. Access Control Man-
ager has the responsibility of creating Access Con-
trol Policies (ACPs) that are compliant by-design with
the GDPR. It works in collaboration with the Con-
sent Manager by receiving, as input, the machine-
readable specification of services definitions and the
related Data Subjects’ consents. More precisely, it
uses: Personal Data related to Data Subject classi-
fied in categories as required by the GDPR; infor-
mation about the Controller of each service and the
defined purposes; the consent given by the Data Sub-
ject in terms of relation between Personal Data and
Purposes. Based on that information, Access Con-
trol Manager is able to create specific Access Con-
trol Policies, each related to a specific article of the
GDPR. The peculiarities of the Access Control Man-
ager are the possibility to (a) be integrated with differ-
ent Consent Managers, and (b) to collaborating with
different Access Control systems. This in order to
guarantee the independence with specific input and
output formats, and to be easily enhanced with stan-
dardized Access Control Systems, such as the one of-
fered by the XACML standard, e.g., when this com-
ponent is missed in the Smart ICT Core System.
In this section, we provide an instantiation of the ar-
chitecture presented in the previous section by us-
ing real artefacts coming from both industrial and
academic contexts described in Section 4.1 and 4.2,
respectively. More precisely, we will show how
(industrial open-source product) and GEN-
ERAL D (Gdpr-based ENforcEment of peRsonAL
Data) framework (academic proposal) can collaborate
and easily be integrated to achieve the GDPR compli-
To better explain the use of CaPe and GEN-
ERAL D framework, we consider the following ap-
plication example sets into a wellness environment.
Alice, a Data Subject, wants to use a smart wellness
application to monitor her daily activities to achieve a
predefined training objective. The application is pro-
vided by the myWellness company (Controller). To
meet Alice’s needs, myWellness has so far defined
different purposes, each related to a specific data set
of Personal Data. At the time of subscribing to the
myWellness application, Alice provided her personal
data (i.e., Age, Gender, and Blood Cholesterol) and
gave her consent for one purpose (i.e., MyCholes-
terol). Additionally, Alice gave her consent to share
her personal data with a third-party company named
zzz-HealthOrg company. In turn, myWellness gave to
Alice controller’s contacts that include: piiController,
orgName, address, e-mail, and phone number.
4.1 Consent Manager: CaPe at Glance
CaPe provides an ICT suite for a consent-based, user-
centric personal data management. It follows My-
principles to exploit the potential of personal
data, facilitates its control and new business oppor-
tunities in compliance with the GDPR. Thus, CaPe
assures the following features: i) Consent authorizes
Data Sources to provision data to Data Consumer and
authorizes Data Requester to process that data; ii)
Consent refers to a Data Usage Policy that can be
linked to consent formalization; iii) Consent is given
in a clear manner so as to let the data controller to
demonstrate that a valid consent has been given; iv)
How to Improve the GDPR Compliance through Consent Management and Access Control
Consent record clearly includes 1. Who consented;
2. When they consented; 3. What was consented;
4. How was consented; 5. Whether a consent with-
drawn occurred.
Figure 3: Overview of the CaPe Consent Manager.
Figure 3 shows an overview of the CaPe Consent
Manager. As in the figure, the CaPe acts as an in-
termediary for the communication between data sub-
jects and data controllers, supporting the generation
and management of dynamic consents.
As shown in Figure 4, the CaPe provides also two
specific dashboards (the Data Controller Dashboard
and the User Self-Service Dashboard) for let the over-
all management of the personal data management.
Additionally, through these interfaces, CaPe provides
specific features to grant and withdraw consent to
third parties for access to data about oneself.
Figure 4: How CaPe Works.
A general CaPe use is provided in Figure 4. In
the depicted scenario, through the Data Controller
Dashboard an organization can model the legal basis
for the processing of personal data: in a standardized
manner; in accordance with the relevant information
(i.e., purpose, processing, type of data and so on); and
in line with the related privacy policy. According to
the derived model, CaPe automatically generates the
consent form that can be shown to the data subject.
The two separated dashboards can let, on one side, the
Data Controller to view and manage all the consents
collected, on the other, the Data Subject, through the
User Self-Service Dashboard, to check which data is
used, how and for what purpose and to manage the
related consents.
Considering the application example mentioned at
the beginning of this section, for confidential reasons
we we report in Figure 5 just an extract of the consent
model derived by CaPe. As in the figure, the Consent
is modeled as an entity having a unique ID identifying
it and a status (Active, Non-Active).
Figure 5: Extract of the CaPe Consent Model.
A Data Subject is identified by its ID, and it is re-
lated to a set of Personal Data, each represented by a
name/value pair. The Data Subject can give a Consent
for processing his/her for a specific Purpose defined
by the Controller. Each Purpose has a name and it
is implemented by means a set of Actions. During the
given consent phase, the Data Subject can choose also
to share his/her Personal Data with one or more Orga-
nizations, so as the controller can eventually achieve
the defined purposes. As defined in Art. 7 of the
GDPR, Data Subject can withdraw at any time the
given consent. In the defined model, this is modeled
as the withdrawnBy association between Data Sub-
jects and Consent entities reported in Figure 5.
In the current implementation, CaPe encodes the
instances of the defined model as Json files, and it then
provides such a files to the GENERAL D Framework
for the aim of making the given consent directly en-
forceable by the Smart ICT Core System.
4.2 Access Control Manager:
GENERAL D Framework
GENERAL D Framework instantiates the Access
control Manager, and it is composed of four main
components (see Figure 6): User Stories Manager;
Json Manager; ACP Manager; and DBs Manager.
User Stories Manager. manages a Data Protection
Backlog that contains GDPR-based User Sto-
ries (Bartolini et al., 2019b). In the considered
implementation, these are specific ACP templates,
each associated with specific GDPR’s provision,
ICISSP 2021 - 7th International Conference on Information Systems Security and Privacy
Figure 6: Overview of GENERAL D Access Control Man-
useful for automating the implementation of stan-
dardized access control policies in compliance
with the regulation. In our case, the here consid-
ered User Stories are templates structured as ab-
stract XACML policies, and they are stored in an
internal database (not shown in Figure 6).
Json Manager. has the responsibility to interact di-
rectly with CaPe described in the previous sec-
tion. It receives the consent in Json format, and it
parses that consent so as to extract the relevant in-
formation for the ACPs generation purpose. Such
information includes, among others, the Consent
ID and the Consent Usage Rules that contain the
defined purposes of processing, the allowed op-
erations, and Personal Data provided by the Data
ACP Manger. is the core component of GEN-
ERAL D framework. It has the responsibil-
ity of creating enforceable ACPs encoded in the
XACML language. It interacts with: 1) Json Man-
ager for retrieving the data to be processed (e.g.,
the Controller’s data, the defined purposes, the list
of allowed third parties); 2) User Stories Manager
for receiving the ACPs templates to be filled with
those data. Therefore, ACP Manager combines
the received data for deriving XACML policies,
that it stores in the Access Control Policies repos-
DBs Manager. offers databases supporting function-
alities to the User Stories Manager and ACP
Manger (e.g., create/modify/delete database, and
insert/modify/delete specific entries in the avail-
able tables). In the considered implementation,
DBs Manager relies on the MySQL Data Base
Management System.
By referring to the Alice’s activities in the my-
Wellness’ application scenario presented at the be-
ginning of this section, and to CaPe’s consent model
shown in Figure 5, the algorithm implemented by
GENERAL D Framework, for deriving enforceable
XACML policies, is reported in Algorithm 1.
From the behavioral point of view, the algorithm
implements three scenarios:
1. Data Subject (Alice) gives his/her consent to Con-
troller (myWellness company). In this case the
XACML-based ACPs are generated from scratch
and loaded into the database. These policies will
be then made enforceable as soon as the Aclice’s
activities start, i.e., during the production phase.
2. Data Subject modifies his/her consent, e.g., Al-
ice wants to modify her given consent so as to
allow the management only to two of the three
initially provided Personal Data. This involves
the withdraw of the previously given consent and
its substitution with a new one. In terms of the
access control policies, this means to modify the
related ACPs in DENY-ALL policies and create
new ACPs for the modified consent. This be-
havior is in line with the accountability princi-
ple, because it lets the controller to demonstrate
the compliance with the GDPR by showing the
history of both the consent and the related ACPs
modifications. Specifically, it refers to the trans-
parency principle (Art. 5.1(a) “lawfulness, fair-
ness and transparency”) and Art. 30 (“Records of
processing activities”).
3. Data Subject (Alice) withdraws the given consent:
i.e., prevent any access to Personal Data belong-
ing the Data Subject. In terms of access control,
this means to deny any access requests to those
data. Practically, this can be enforced by the ACP
Manager by setting the related ACPs to DENY-
From a procedural point, as shown in Algorithm 1,
through the Json Manager component, GENERAL D
parses the Json file for retrieving the data of inter-
est, i.e., Personal Data, Purposes and the third par-
ties those data are shared with. Then, through the
joint collaboration of ACP Manager and User Stories
Manager, the ACPs templates can be instantiated for
generating XACML-based policies that are compli-
ant with the GDPR (GENERAL D Framework’s out-
In details, the Algorithm 1 (line 1) takes as in-
put the consent represented in Json format (CJF), and
it parses that file by obtaining its internal represen-
tation (CJFAsPOJO, line 4). Then, in case of active
consent (Algorithm 1, line 6), the algorithm verifies
whether the processed consent is a modification of
an already given one. In case of modification, the
related ACPs derived so far are modified to DENY-
ALL policies (Algorithm 1, line 8). Consequently,
How to Improve the GDPR Compliance through Consent Management and Access Control
for each User Story and for each consent related to
a specific purpose, an XACML policy is generated
(Algorithm 1, line 11-16). In case of withdrawing
the content, the received Json input contains the sta-
tus non-Active (Algorithm 1, line 17), and in terms of
AC, this means that no one is able to access Personal
Data related Data Subject. This is reflected in deny-
ing all the incoming access requests, by triggering the
default DENY-ALL policies modified in Algorithm 1,
line 18.
Algorithm 1: GDPR-based ACP Derivation.
1: input: CJF Consent as Json File
2: output: GAL GDPR-based ACP List of XACML
3: GAL {}
4: CJFAsPOJO parse(CJF)
6: if CJFAsPOJO.isActive() then
7: if isAlreadyGiven(cID) then
8: DenyAllPolicies(cID)
9: end if
10: UST L loadUserStoriesTempaltes()
11: Foreach ust
UST L do
12: Foreach c
13: ACP CreateACPS(ust
, c
, cID)
14: GAL.add(ACP)
15: end for
16: end for
17: else if !CJFAsPOJO.isActive() then
18: DenyAllPolicies(cID)
19: end if
20: return GAL
By referring to the previously presented Use Case
scenario, and by applying Algorithm 1, we illustrate
in Figure 7 one of the obtained ACPs in XACML-
like format. This policy rules access to the Personal
Data as defined in Art. 6.1(a), i.e., when the process-
ing activity is lawful based on the consent given by the
Data Subject. As in the figure, the allowed subjects,
to access Alice’s Personal Data, are both myWellness
(Controller) and zzz-HealthOrg (Third Party). This is
expressed in the Target element of the XACML pol-
icy. As specified in the derived rule, the purpose of
processing (i.e., MyCholesterol purpose) is achieved
by allowing access to perform a specific set of Ac-
Smart ICT Systems are gaining a certain amount of
attention in the last years. They provide means for
developing Smart Services in different domains such
as Smart-Cities, Education, and Healthcare environ-
Policy . . . . . . . . . . . . . . . . . . . . . . PolicyId = alicePolicy
root element
Target . . . . . . . . . . . . . . . . Lawfulness of Processing Sample Pol-
Subject . . . . . . . . . . . Controller.orgName = myWellness
Subject . . . . . . . . . . . ThirdParty.orgName = zzz-HealthOrg
Rule . . . . . . . . . . . . . . . . . . . RuleId = readRule, Effect = Permit
Target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Resource . . . . . Age
Resource . . . . . Gender
Resource . . . . . Blood Cholesterol
Action . . . . . . . Action.Purpose=MyCholesterol
Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
And . . . . . . . . . . . And Operator
string-one-and-only . . . . . . . . . . . . . . . . . . . . . . .
type-One-And-Only Function.
#Resource = 1
string-equal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
type-Equal Function.
Resource.owner = AliceID
string-is-in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rule . . . . . . . . . . . . . . . . . . . RuleId = defaultRule, Effect = Deny
default: deny all, which is not allowed
Figure 7: An XACML-like Policy authorizing Lawfulness
of processing of Personal Data based of the Consent Given
by the Data Subject (Art. 6.1(a)).
ments, to cite a few. However, with the entering into
the force of the EU data protection regulation, i.e.,
the GDPR, proposed solutions for Smart ICT Systems
lack appropriate supports to aid Controllers in devel-
oping Smart Services. More precisely, the proposed
solutions are not Privacy-By-Design conceived, i.e.,
the implemented services are not compliant with the
GDPR from the early stage of their design. To over-
come these difficulties, we have conceived a possi-
ble generic architecture that can be customized with
real artifacts to accomplish the GDPR compliance.
We have also provided a proof-of-concept consist-
ing of the integration of two new tools coming from
the industrial and academic sectors: CaPe and GEN-
ERAL D Framework. This integration has demon-
strated the applicability and flexibility of our Privacy-
By-Design solution for Smart ICT Systems.
For future work, we are planning to validate our
approach by considering a Smart-Cities environment.
In particular, the integration will be validated in the
currently available and emerging Smart-Cities plat-
forms, such as the ones based on the market-ready
open-source software FIWARE platform.
ICISSP 2021 - 7th International Conference on Information Systems Security and Privacy
This work is partially supported by CyberSec4Europe
H2020 Grant Agreement No. 830929.
Akerlund, A. and Große, C. (2020). Integration of data
envelopment analysis in business process models: A
novel approach to measure information security. In
ICISSP, pages 281–288.
Barsocchi, P., Calabr
o, A., Ferro, E., Gennaro, C.,
Marchetti, E., and Vairo, C. (2018). Boosting a low-
cost smart home environment with usage and access
control rules. Sensors, 18(6):1886.
Bartolini, C., Calabr
o, A., and Marchetti, E. (2019a). En-
hancing business process modelling with data protec-
tion compliance: An ontology-based proposal. In Pro-
ceedings of the 5th International Conference on Infor-
mation Systems Security and Privacy, ICISSP 2019,
Prague, Czech Republic, February 23-25, 2019.,
pages 421–428.
Bartolini, C., Daoudagh, S., Lenzini, G., and Marchetti, E.
(2019b). Gdpr-based user stories in the access con-
trol perspective. In Piattini, M., da Cunha, P. R.,
de Guzm
an, I. G. R., and P
erez-Castillo, R., editors,
Quality of Information and Communications Technol-
ogy - 12th International Conference, QUATIC 2019,
Ciudad Real, Spain, September 11-13, 2019, Proceed-
ings, volume 1010 of Communications in Computer
and Information Science, pages 3–17. Springer.
Bartolini, C., Daoudagh, S., Lenzini, G., and Marchetti, E.
(2019c). Towards a lawful authorized access: A pre-
liminary gdpr-based authorized access. In van Sin-
deren, M. and Maciaszek, L. A., editors, Proceed-
ings of the 14th International Conference on Software
Technologies, ICSOFT 2019, Prague, Czech Republic,
July 26-28, 2019, pages 331–338. SciTePress.
o, A., Daoudagh, S., and Marchetti, E. (2019). Inte-
grating access control and business process for GDPR
compliance: A preliminary study. In Proceedings of
the Third Italian Conference on Cyber Security, Pisa,
Italy, February 13-15, 2019.
o, A., Marchetti, E., Moroni, D., and Pieri, G. (2019).
A dynamic and scalable solution for improving daily
life safety. In Proceedings of the 2nd International
Conference on Applications of Intelligent Systems,
pages 1–6.
Carauta Ribeiro, R. and Dias Canedo, E. (2020). Using
mcda for selecting criteria of lgpd compliant personal
data security. In The 21st Annual International Con-
ference on Digital Government Research, dg.o ’20,
page 175–184, New York, NY, USA. Association for
Computing Machinery.
Dernaika, F., Cuppens-Boulahia, N., Cuppens, F., and Ray-
naud, O. (2020). Accountability in the A posteriori
access control: A requirement and a mechanism. In
Quality of Information and Communications Technol-
ogy - 13th International Conference, QUATIC 2020,
Faro, Portugal, September 9-11, 2020, Proceedings,
volume 1266 of Communications in Computer and In-
formation Science, pages 332–342. Springer.
European Union (2016). Regulation (EU) 2016/679 of the
European Parliament and of the Council of 27 April
2016 (General Data Protection Regulation). Official
Journal of the European Union, L119:1–88.
Greaves, B., Coetzee, M., and Leung, W. S. (2018). Access
control requirements for physical spaces protected by
virtual perimeters. In Furnell, S., Mouratidis, H., and
Pernul, G., editors, Trust, Privacy and Security in Dig-
ital Business, pages 182–197, Cham. Springer Inter-
national Publishing.
Haofeng, J. and Xiaorui, G. (2019). Wi-fi secure access con-
trol system based on geo-fence. In 2019 IEEE Sym-
posium on Computers and Communications (ISCC),
pages 1–6.
Jensen, C. D., Geneser, K., and Willemoes-Wissing, I. C.
(2013). Sensor enhanced access control: Extend-
ing traditional access control models with context-
awareness. In Fern
andez-Gago, C., Martinelli, F.,
Pearson, S., and Agudo, I., editors, Trust Management
VII, pages 177–192, Berlin, Heidelberg. Springer
Berlin Heidelberg.
Krenn S. et al. (2020). Deliverable D3.2: Cross Sec-
toral Cybersecurity Building Blocks. https://
2-Cross sectoral cybersecurity-building-blocks-v2.0.
uttler, J., Fischer, R., Ganz, W., and Urmetzer, F.
(2020). Perceived quality of artificial intelligence in
smart service systems: A structured approach. In
Shepperd, M., Brito e Abreu, F., Rodrigues da Silva,
A., and P
erez-Castillo, R., editors, Quality of Infor-
mation and Communications Technology, pages 3–16,
Cham. Springer International Publishing.
OASIS (2013). eXtensible Access Control Markup Lan-
guage (XACML) Version 3.0. http://docs.oasis-open.
Samir Labib, N., Liu, C., Esmaeilzadeh Dilmaghani,
S., Brust, M., Danoy, G., and Bouvry, P. (2018).
White paper: Data protection and privacy in smart
ict-scientific research and technical standardization.
Technical report, ILNAS.
Sforzin A. et al. (2020). Deliverable D3.11: Definition of
Privacy by Design and Privacy Preserving Enablers.
Sokolovska, A. and Kocarev, L. (2018). Integrating tech-
nical and legal concepts of privacy. IEEE Access,
How to Improve the GDPR Compliance through Consent Management and Access Control