A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem

big data and
cognitive computing
Article
A Dynamic Intelligent Policies Analysis Mechanism
for Personal Data Processing in the IoT Ecosystem
Konstantinos Demertzis 1,* , Konstantinos Rantos 1 and George Drosatos 2
1 Department of Computer Science, International Hellenic University, 65404 Kavala, Greece; krantos@cs.ihu.gr
2 Institute for Language and Speech Processing, Athena Research Centre, 67100 Xanthi, Greece;
gdrosato@athenarc.gr
* Correspondence: kdemertzis@teiemt.gr; Tel.: +30-694-824-1881
Received: 21 March 2020; Accepted: 20 April 2020; Published: 27 April 2020
Abstract: The evolution of the Internet of Things is significantly affected by legal restrictions imposed
for personal data handling, such as the European General Data Protection Regulation (GDPR).
The main purpose of this regulation is to provide people in the digital age greater control over their
personal data, with their freely given, specific, informed and unambiguous consent to collect and
process the data concerning them. ADVOCATE is an advanced framework that fully complies with
the requirements of GDPR, which, with the extensive use of blockchain and artificial intelligence
technologies, aims to provide an environment that will support users in maintaining control of their
personal data in the IoT ecosystem. This paper proposes and presents the Intelligent Policies Analysis
Mechanism (IPAM) of the ADVOCATE framework, which, in an intelligent and fully automated
manner, can identify conflicting rules or consents of the user, which may lead to the collection
of personal data that can be used for profiling. In order to clearly identify and implement IPAM,
the problem of recording user data from smart entertainment devices using Fuzzy Cognitive Maps
(FCMs) was simulated. FCMs are an intelligent decision-making system that simulates the processes
of a complex system, modeling the correlation base, knowing the behavioral and balance specialists
of the system. Respectively, identifying conflicting rules that can lead to a profile, training is done
using Extreme Learning Machines (ELMs), which are highly efficient neural systems of small and
flexible architecture that can work optimally in complex environments.
Keywords: Internet of Things; privacy; GDPR; digital consents management; fuzzy cognitive map;
extreme learning machine; feed-forward neural network
1. Introduction
Over the last fifteen years, the emergence of the Internet has led to changes in all areas of everyday
life, affecting the lives of ordinary citizens, and within the next decade, the Internet of Things (IoT)
revolution will affect the energy, agriculture and transport sectors, as well as the more traditional
sectors of the economy and society. In this spirit, we are faced with a phenomenon of great economic
and social potential that presents great opportunities but also important challenges associated with
unimportant risks, of multidimensional and horizontal nature, which affect businesses and consumers,
administrations and citizens alike [1]. For example, the rapid increase in the number of devices
capable of collecting personal data from the user’s environment is one of the most basic and most
serious modern forms of threat to the users’ privacy. Participating devices in the IoT ecosystem share
information that can be used to track activities and permanently record the status of users and their
characteristics or activities. The data in question can be used to make user profiles, with or without
their agreement, and enable automated decision-making for third parties [2].
Big Data Cogn. Comput. 2020, 4, 9; doi:10.3390/bdcc4020009 www.mdpi.com/journal/bdcc

Big Data Cogn. Comput. 2020, 4, 9 2 of 16
Following the enforcement of General Data Protection Regulation (GDPR), consumers have
enhanced their ability to control their personal data and personal preferences, as they can control
how the data produced by their IoT devices are used, and to whom and why access to these data is
provided [3]. Thus, the legal framework safeguards the absolute right of users to privacy and the
protection of their personal data, while providing rights to information access and to be forgotten.
However, there are no avoidable breaches related to profiling, with a view to fully automated
decision-making for data subjects.
The GDPR on Article 4(4) says that profiling is [4]: “Any form of automated processing of personal
data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person,
in particular to analyze or predict aspects concerning that natural person’s performance at work, economic
situation, health, personal preferences, interests, reliability, behavior, location or movements”. Specifically,
organizations gain personal information about entities from a variety of different sources, such as
internet searches, buying behaviors, lifestyle, and activities. This data can be gathered from mobile
phones, social networks, video surveillance systems, and the IoT applications and can be analyzed to
classify persons into diverse groups that links different characteristics to create profiles for individuals.
Although the majority of Internet users are particularly worried about the exposure of their
personal data, since they consider their privacy to be an important issue and want to have control over
their personal data, most face threats related to them passively, perhaps even sluggishly. The main
reason is their inability to understand the means and techniques used for collecting their personal
data, how these data are being used, and most importantly, they are unaware of ways to take active
measures to effectively protect their privacy.
In the recent past, several researchers pointed out that users are usually unaware of profiling and
tracking activities, which may be carried out by malicious stakeholders even when the user relies on
(seemingly) anonymous procedures [5]. For example, profiling issues can be handled well based on
privacy-related methods, such as k-anonymity [6] or pseudo-anonymity [7] in the privacy preservation
of published data.
Another key aspect involving privacy is that, often, data features multiple individuals or, more
specifically, data that seems to involve only certain individuals, in fact, reveal information about
others [8]. For instance, in [9], the authors introduced the problem of collaborative privacy on the social
networks and provide photo-sharing as a case study. Also, Thomas et al. [10], to handle the problem of
multi-party privacy and the conflicting settings between online friends, proposed an approach that
takes into consideration the strength of the social ties between online users.
Accordingly, there is a serious gap in applied research and, more generally, in implementations or
applications that could help the user obtain meaningful information on how to deal with personal data
leakage incidents and how optimal decisions are made with regards to the protection of their personal
data, as well as applications that will undertake a thorough analysis of the user’s consent, in order to
identify possible actions aimed at profiling [11].
This paper presents the Intelligent Policies Analysis Mechanism (IPAM) of the ADVOCATE
framework [12–14], which, in an intelligent and fully automated manner, can identify conflicting rules
of the user’s consents, which may lead to the collection of personal data and, consequently, be used
for profiling. The main goal of the proposed approach is the design of an intelligent decision-making
system for protecting the privacy of average users. This is achieved by simulating the processes of
smart entertainment devices that can collect personal data from the user’s environment. The IPAM is
an innovative, reliable, low-demand and highly effective system based on sophisticated computational
intelligent methods that deliver high-precision results, capable of responding to the problem, as well
as in cases of similar complex situations.
The rest of this paper is structured as follows: Section 2 presents the related work in the field.
Section 3 introduces the ADVOCATE framework and its main components. Section 4 describes the
scenarios and data used in our analysis. Section 5 defines the methodology of Intelligent Policies
Analysis Mechanism (IPAM) based on Fuzzy Cognitive Maps (FCMs) and Extreme Learning Machines

(ELMs) methods, while Section 6 presents the results of our approach. Finally, Section 7 concludes the
paper and outlines future research objectives.
2. Related Work
An intrinsic characteristic of the IoT is persistent gathering and linkage of user data to provide
adapted capabilities. The need to offer users the capability to control the private data produced by IoT
devices, is widely recognized [11]. These aspects of the IoT can pose risks to user privacy. Theoretically,
aggressive inferences can be drawn from related datasets, comprising data produced through usage of
connected devices and services. The GDPR encloses plentiful provisions relevant to the hazards posed
by smart identification technologies [15]. Nevertheless, the strict legal requirements well-defined in the
GDPR may be inadequate to certify a fair balance among user’s interests in privacy and the interests
of IoT developers and data controllers [3]. Several research efforts have been made in the direction
of providing appropriate solutions for security and privacy in the IoT ecosystem since this is an area
that currently attracts many research initiatives [16,17]. For example, in [18], the authors present a
JAVA-based security model that handles some of the data security and privacy issues of an open, secure
and decentralized system for managing resource sharing among related procedures in the fog-to-cloud
(F2C) environment and GDPR, which is called mF2C. The model employs a PKI-based trust model
to enable authentication and authorization. It uses policy to certify data privacy and cryptography
to provide data confidentiality, integrity, and non-repudiation. However, the proposed framework
neither embraces data protection functionalities from the security perspective, nor controls blockchain
technology to augment mF2C security and data protection capabilities. In contrast, the ADVOCATE
framework proposed by the authors of this paper [12–14], lays the ground for the formation of trust
relationships among data subjects and controllers to the GDPR-compliant IoT ecosystem, utilizing
blockchain technology to support the integrity, the non-repudiation and the versioning of consents in a
publicly verifiable manner.
Moreover, in [19], Subahi and Theodorakopoulos proposed a novel method for ensuring
compliance of IoT data disclosure to the agreeing privacy policy. The method was used to analyze
the network traffic between the IoT ecosystem and its applications with their distinct Privacy Policy
Agreements (PPA). The authors suggest eight norms and compare them with the actual PPA carried
out by each IoT subsystem.
Also, therearemachinelearningbasedmethodsforIoTdeviceswithbetterprivacyandsecurity [20–23].
For example, Guntamukkala et al. [24] suggested a machine-learning based method for evaluating
the completeness of online privacy policies utilizing an automated approach. The term completeness
refers to the presence of eight sections in an online privacy policy that have been recognized as helpful
in ensuring the transparency of a privacy policy. The proposed system employs a machine-learning
framework to predict a completeness score for the privacy policy and the risk to their privacy. However,
the completeness by itself will not warrant transparency, and it may still be uncertain, resulting in an
inferior degree of transparency. Therefore, the proposed method needs to be enhanced with the ability
to automatically calculate other components that contribute to privacy policy transparency.
In addition, Fuzzy Cognitive Maps (FCMs) are used in the literature to calculate cyber and privacy
risks. For example, the aim of the study of the Schläger and Pernul [25] is to show aptitudes for
e-commerce organizations or groups using an attribute-based authentication and authorization method
to use client data for the derivation of metric trust and reputation values. Using FCMs and trust
metrics, the method integrates an organization’s user data within an easy to use service provider
interface for reputation management. To assure user privacy, the data is categorized and stored in a
distributed manner.
ADVOCATE [12–14] addresses the challenges related to privacy protection in the IoT ecosystem,
mainly with respect to the supervision of consents as GDPR requires and tries to close a significant
gap in this area. It allows users to organize their consents and express their personal data disposal

policies considering the corresponding system recommendations. Similarly, data controllers can utilize
ADVOCATE to ensure GDPR-compliance in managing personal data.
3. The ADVOCATE Framework
The ADVOCATE framework [12–14] aims to meet the main requirements of the GDPR with
regards to obtaining and managing data subjects’ consents. According to the GDPR, consents are data
subject’s wishes that must be freely given, speciﬁc and unambiguous. Moreover, prior to obtaining
data subjects’ consents, data controllers must appropriately inform them in a transparent manner about
(a) personal data they are willing to manage and the sources thereof, (b) the purposes, time periods
and legal basis of the processing, (c) the entities that will process it, and (d) recipients or categories
of data recipients. ADVOCATE aims to provide users with the means to manage their consents and
shape their personal data disposal rules/policies.
An ADVOCATE cloud service approach capable of handling personal data collected by IoT
sensors deployed in smart cities and e-health environments is illustrated in Figure 1.
Big Data Cogn. Comput. 2020, 4, x FOR PEER REVIEW 4 of 16
3. The ADVOCATE Framework
The ADVOCATE framework [12–14] aims to meet the main requirements of the GDPR with
regards to obtaining and managing data subjects’ consents. According to the GDPR, consents are data
subject’s wishes that must be freely given, specific and unambiguous. Moreover, prior to obtaining
data subjects’ consents, data controllers must appropriately inform them in a transparent manner
about (a) personal data they are willing to manage and the sources thereof, (b) the purposes, time
periods and legal basis of the processing, (c) the entities that will process it, and (d) recipients or
categories of data recipients. ADVOCATE aims to provide users with the means to manage their
consents and shape their personal data disposal rules/policies.
An ADVOCATE cloud service approach capable of handling personal data collected by IoT
sensors deployed in smart cities and e-health environments is illustrated in Figure 1.
Industry / Enterprises /
Government
Data ControllersUser-side IoT Environment
ADVOCATE
Data Storage
& Processing
Data Management Platform
Device Registration and
Management
Consent/Policy
Management
Consent
Integrity and
Non-
repudiationIntelligent
Policies Analysis
Data Collection and Provision
Services
Blockchain Infrastructure
Block
tx st rc tx
st sttx tx blocks with contract states (st),
transactions (tx), receipts (rc), etc.
Block Block Block Block Block
time
Consents Requests and
Management
IoT Data Flow
Hashes
DataandPrivacyProtection
Figure 1. ADVOCATE framework conceptual architecture.
An interaction of core ADVOCATE components during the introduction of new consents, is
presented in Figure 2.
Figure 1. ADVOCATE framework conceptual architecture.
In order to provide immutable versioning control, identifying the latest consents as well as periods
that speciﬁc consents were valid in an undisputable manner, the ADVOCATE platform uses blockchain
technology with smart contracts. This procedure is illustrated in Figure 3.
An interaction of core ADVOCATE components during the introduction of new consents, is
presented in Figure 2.

ADVOCATE Activity Diagram
ADVOCATE Core
Platform
Data ControllerData Subject
ADVOCATEConsentCreationandPolicyUpdate
IoT device
discovery
Device
registration
Register device
Provide device
details
Place request
Format request /
inform data subject
Present to data
subject
Data subject
decision
Stakeholders'
signature
Sign consent
Prepare
smart contract
input values
Store to
Blockchain
Update user
policy Consent
Management
Consent
Notary
Sign data processing
purposes, periods
Consent
Management
Make device
searchable
Request Analysis &
Recommendations
Intelligent
Policies
Analysis
Figure 2. Interaction of ADVOCATE components during the introduction of new consents.
In order to provide immutable versioning control, identifying the latest consents as well as
periods that specific consents were valid in an undisputable manner, the ADVOCATE platform uses
blockchain technology with smart contracts. This procedure is illustrated in Figure 3.
Smart Contract
Hashing and
interaction
process with
the blockchain
consent initialisation,
update or withdrawal
Consent on data
processing
details
Signing
by Data
Controller and
Subject
Signatures
of consent
+
Data Contract
Data Contract
manages all subject's
consents for a
specific IoT device
Consent’s hash
2nd
version
Consent’s hash
last version
of controller K
of controller K
Data Contract
of controller K
Consent’s hash
1st
version
Data Contract
of controller K
Consent’s hash
2nd
version
Data Contract
of controller K
Consent’s hash
lastversion
Data Subject
Data Controller
Figure 3. The steps followed by the consent notary component to ensure integrity, non-repudiation,
and versioning of given contents.
ADVOCATE Activity Diagram
ADVOCATE Core
Platform
Data ControllerData Subject
ADVOCATEConsentCreationandPolicyUpdate
IoT device
discovery
Device
registration
Register device
Provide device
details
Place request
Format request /
inform data subject
Present to data
subject
Data subject
decision
Stakeholders'
signature
Sign consent
Prepare
smart contract
input values
Store to
Blockchain
Update user
policy Consent
Management
Consent
Notary
Sign data processing
purposes, periods
Consent
Management
Make device
searchable
Request Analysis &
Recommendations
Intelligent
Policies
Analysis
In order to provide immutable versioning control, identifying the latest consents as well as
periods that specific consents were valid in an undisputable manner, the ADVOCATE platform uses
blockchain technology with smart contracts. This procedure is illustrated in Figure 3.
Smart Contract
Hashing and
interaction
process with
the blockchain
consent initialisation,
update or withdrawal
Consent on data
processing
details
Signing
by Data
Controller and
Subject
Signatures
of consent
+
Data Contract
Data Contract
manages all subject's
consents for a
specific IoT device
Consent’s hash
2nd
version
Consent’s hash
last version
of controller K
of controller K
Data Contract
of controller K
Consent’s hash
1st
version
Data Contract
of controller K
Consent’s hash
2nd
version
Data Contract
of controller K
Consent’s hash
lastversion
Data Subject
Data Controller

Finally, the intelligence policies analysis component of the ADVOCATE framework is responsible
for performing the necessary analysis for the user’s data disposal policy, via an Intelligent Policies
Analysis Mechanism (IPAM), to detect conflicting or contradictory rules/policies of a user’s consents.
The implementation of IPAM, that is presented in this work, is based on an advanced simulation
of the problem of personal data leakage from smart entertainment devices. The problem is modeled
using Fuzzy Cognitive Maps (FCMs), which is a technique that belongs to the neuro-fuzzy systems
and can be used to solve decision-making problems and model and simulate complex systems [26].
FCMs provide causal acquisition and representation of knowledge, and they can support the causal
knowledge reasoning process. The identification process of conflicting rules is accomplished using
Extreme Learning Machines (ELMs), which is a very fast and efficient type of Single-Hidden Layer
Feed-Forward Neural Network (SHLFFNN) where the parameters of hidden nodes can be randomly
assigned and never updated or can be inherited from their ancestors without being changed.
4. Scenarios and Data
Smart entertainment devices greatly upgrade users’ home entertainment experience by giving
them full control over all the devices of a home ecosystem through voice, applications, and remote
control. At the same time, privacy concerns are constantly increasing, arising from the fully accessible
and interconnected home environment of smart devices, which produce or record an increasing number
of data. These concerns should also be recorded in cases of data collection without the user’s full
awareness or consent but also by the ignorance of the consequences of their sharing.
In order to clearly identify and implement the proposed IPAM, the problem of recording user data
from smart entertainment devices was simulated. After a thorough study of the operation of these
devices, bibliographic review and analysis of their manuals, the following variables, which can be
collected by smart entertainment devices, were chosen to model the problem of profiling:
User Identity Data
1. Username. The username of the user.
2. Sex. The sex of the user.
3. Age. The age of the user.
Location Data
4. Location_History. If the machine caches the location history of its usage.
Status Data
5. Device_Number. The unique address of the device (mac address).
6. Device_Type. Device type, for example, TV.
7. IP_Address. The IP address of the device.
8. Operating System. The device’s OS.
Actionable Data
9. Automation. If the device starts/stops automatically.
10. Average Time. The average time that the device is being used during a day.
11. Average Duration. The average duration of using an application on the device, e.g., Youtube
or Netflix.
12. Interests. If the user registers his/her interest on a service, e.g., action movies on Netflix.
Class
13. Yes or No. Automated individual decision-making, including profiling.

All of the above variables are binary data, i.e., either collected (1) or not (0). Table 1 shows some
cases of the parameters that make up the problem.
Table 1. Parameters cases.
Username Sex Age
Location
_History
Device
_Number
Device
_Type
IP OS Automation
Average
_Time
Average
_Duration
Interests
1 1 1 1 0 1 0 1 1 1 0 0
1 1 1 0 1 1 0 1 1 0 0 0
1 1 0 1 0 1 0 1 1 0 0 0
1 0 0 1 1 1 1 0 0 0 0 0
In fact, the above table depicts the symbolic description and representation of the problem
configuration described, which considers the combination of the cases that may arise from the
recording or not of the corresponding characteristics. Specifically, and given that our problem contains
12 parameters (username, sex, age, location_history, device_type, device_type, IP, OS, automation,
average_time, average_duration, interests) that can receive two possible states, the total dataset includes
212 possible situations, i.e., 4096 cases.
Before proceeding to the problem modeling methodology described in Section 5, it should be
noted that the implementation of the scenarios was based solely on a heuristic, approximate method
of analysis.
The scenario of the 12 specific parameters with two possible states implements a model that is an
abstract representation of the real system. This denotes that it implements only certain properties and
characteristics of the real system, without taking into account all of them. The need for selection arises
from the fact that real scenarios are extremely complex and cannot be fully represented.
Therefore, the model simplifies reality by choosing a part of it which is suitable for:
1) Giving a general rather than a generalized view of the problem.
2) Focusing on the parameters of how smart entertainment devices work, which is one of the
objectives of this research.
3) Creating a technically robust dataset, which can, without serious disadvantages, properly train
learning algorithms, while correspondingly being characterized as a relatively high complexity set.
4) Capturing a clear, relatively detailed, and practically possible picture of an unconventional
security problem for which no technical details, methodology references, and experimental data
are available.
It should be noted that there are exponentially multiple and different cases of variables that can
be plotted as system parameters and give a different view of the problem.
The heuristic methodology presented below is indicative of a way of modeling the problem in
question. In general, a heuristic technique is any approach to problem-solving that utilizes a practical
method without guaranteed to be optimal but is adequate for the immediate goals. Our methodology
aims to identify conflicting rules or consents of the user, which may lead to the collection of personal
data that can be using the above research design parameters in a certain way to serve this purpose.
Specifically, it suggests some basic rules, which are specified as a method of modeling complex systems
capable of describing the causal relationships among major concepts that determine the dynamic
behavior of the smart entertainment systems. The proposed rules (as an alternative feature selection
process) describe the causal relationships among the parameters of the problem we are examining.
These causal relationships, which have arisen from existing knowledge and experience, considering
the certainty, uncertainty, and risk of each scenario while focusing on its simplicity, illustrates the
different aspects of the operation of smart entertainment devices. In order to capture this knowledge,
a directed graph is created, with the nodes representing the variables of the problem. An integral
part of this methodology is the process of verifying it with tests of validity, reliability, and a range of
findings. The main purpose of this is to predict the behavior of the system under the given recorded
conditions, or not, of the system parameters.

5. Methodology of IPAM Based on FCM and ELM
The problem of recording user data from smart entertainment devices was modeled using FCMs.
FCMs are an alternative method of modeling complex systems capable of describing the causal
relationships among major concepts that determine the dynamic behavior of a system. In particular,
they form a symbolic description and representation of the formation of a dynamic system. The model
concepts that illustrate the different aspects of a system’s behavior, as well as how these concepts
feedback, either in interaction with each other or in the general dynamics of the system. Human
experience and the knowledge of experts who know the functioning of the system and its behavior in
different situations are simulated by the use of unclear rules where each rule represents an optimal
state or feature of the system.
An FCM [27] consists of a set of neural processing entities called concepts (neurons),
Ci, i = 1, 2, 3, . . . , N, where N is the total number of concepts, which are properties of the system to be
modeled. The concepts are joined together by links with specific weights, which indicate the effect of
the concepts on each other. There are three possible types of causal relationships between two concepts
Ci and Cj:
they form a symbolic description and representation of the formation of a dynamic system. The
model concepts that illustrate the different aspects of a system’s behavior, as well as how these
concepts feedback, either in interaction with each other or in the general dynamics of the system.
Human experience and the knowledge of experts who know the functioning of the system and its
behavior in different situations are simulated by the use of unclear rules where each rule represents
an optimal state or feature of the system.
An FCM [27] consists of a set of neural processing entities called concepts (neurons), 𝐶 , 𝑖 =
1,2,3, … , 𝑁, where Ν is the total number of concepts, which are properties of the system to be modeled.
The concepts are joined together by links with specific weights, which indicate the effect of the
concepts on each other. There are three possible types of causal relationships between two concepts
𝐶 and 𝐶 :
 Positive. It states that an increase or decrease in the value of a cause causes the effect to move in the
same➣ direction and is described with a positive weight 𝑊 .
 Negative. It states that changes in cause and effect concepts occur in opposite directions, with weight
𝑊 having a negative sign.
 Non-existent. It denotes a zero-weight interface. The weight value, e.g., 𝑊 , describes whether the
concept 𝐶 affects the meaning of 𝐶 and is defined in the interval [−1, 1].
At any point in time, the value of each concept 𝛢 is calculated from the sum of the effects of all
other concepts and the limitation of the total effect, by using a blocking function f according to the
following rule:
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴 (1)
where 𝐴 and 𝐴 are the values of concept 𝐶 at times 𝑡 + 1 and 𝑡, respectively, 𝐴 is the value
of concept 𝐶 at time 𝑡, 𝑊 is the weight arc from concept 𝐶 to concept 𝐶 and f is a threshold
function used to limit the value of the concept to a certain range, typically in the interval [0, 1].
In each step, a new state of the concepts arises and after a certain number of iterations, the FCM
may end up either at a particular point of equilibrium or in a limited circle or in chaotic behavior.
When the FCM reaches a certain point of equilibrium, it is concluded that the map has converged,
and the final state corresponds to the actual state of the system to which it changes when the initial
weight values are applied to the map.
The design of FCMs relies heavily on the experience and expertise of specialists who have
sufficient knowledge to model a system and provide initial weights for interrelationships among
concepts. In more flexible FCMs, these weights are calculated through a training process similar to
neural network training methods and algorithms. If the FCM reaches a fixed-point attractor, it is
converged. Otherwise, the updating process is terminated after reaching a maximum number of
iterations. The FCMs’ training algorithm is based on the Hebb Active Learning rule [28].
The Hebb algorithm considers that each node is activated asynchronously. This means that the
balance of the map will be achieved by activating different nodes at different times. Therefore, based
on this algorithm, the nodes of an FCM are distinguished in nodes that are activated and in nodes
being activated. In this case, the node values update rule is modified based on the following function:
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴
( )
(2)
Positive. It states that an increase or decrease in the value of a cause causes the effect to move in
the same direction and is described with a positive weight Wij.
𝐶 and 𝐶 :
following rule:
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴 (1)
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴
( )
(2)
Negative. It states that changes in cause and effect concepts occur in opposite directions, with
weight Wij having a negative sign.
𝐶 and 𝐶 :
following rule:
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴 (1)
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴
( )
(2)
Non-existent. It denotes a zero-weight interface. The weight value, e.g., Wij, describes whether
the concept Ci affects the meaning of Cj and is defined in the interval [−1, 1].
At any point in time, the value of each concept Ai is calculated from the sum of the effects of all
other concepts and the limitation of the total effect, by using a blocking function f according to the
following rule:
At+1
i
= f


At
i +
i=1, i j
WjiAt
j


(1)
where At+1
i
and At
i
are the values of concept Ci at times t + 1 and t, respectively, At
j
is the value of
concept Cj at time t, Wji is the weight arc from concept Cj to concept Ci and f is a threshold function
used to limit the value of the concept to a certain range, typically in the interval [0, 1].
and the final state corresponds to the actual state of the system to which it changes when the initial
sufficient knowledge to model a system and provide initial weights for interrelationships among
concepts. In more flexible FCMs, these weights are calculated through a training process similar to
neural network training methods and algorithms. If the FCM reaches a fixed-point attractor, it is
balance of the map will be achieved by activating different nodes at different times. Therefore, based
being activated. In this case, the node values update rule is modified based on the following function:
At+1
i
= f


At
i +
N
i=1, i j
WjiA
act(t)
j


(2)

where the act index indicates the enabled node. The rule for weight updates, based on this algorithm,
is in the form:
w
(t+1)
ij
= 1 − γ(t)
w
(t)
ij
+ η(t)
A
(t)
i
A
(t)
j
− w
(t)
ij
A
act(t)
i
(3)
where the learning rate η and the weight-reducing factor in the iteration t are calculated based on the
following equations:
η(t)
= b1e(−λ1t)
(4)
γ(t)
= b2e(−λ2t)
(5)
where 0.01 < b1 < 0.09, 0.1 <λ1 < 1, while b2, λ2 are positive constants selected by test and observation.
Based on the above theoretical background, an FCM has been created that describes the causal
relationships among the parameters of the problem we are examining. The causal relationships,
which have arisen from existing knowledge and experience, illustrate the different aspects of the
functioning of smart entertainment devices and the way in which these concepts are feedback, either
in their interactions or in the overall dynamics of the system. To capture this knowledge, a directed
graph was created, with the nodes representing the variables of the problem. The main goal of this
is to predict the behavior of the system under the given recording conditions or not of the system
parameters. Learning has been gained through processes that are the result of continuous interventions,
including the modeled and specialists’ views until the system is balanced.
In simple terms, the written relationships of the map in Figure 4 outline the human experience
and knowledge to solve the problem as a result of continuous observation and intervention [29,30].
where the act index indicates the enabled node. The rule for weight updates, based on this algorithm,
is in the form:
( )
= 1 − ( ) ( )
+ ( ) ( ) ( )
−
( ) ( )
(3)
where the learning rate η and the weight-reducing factor in the iteration t are calculated based on the
following equations:
( )
= ( ) (4)
( )
= ( ) (5)
where 0.01 < b1 < 0.09, 0.1 <λ1 < 1, while b2, λ2 are positive constants selected by test and observation.
Based on the above theoretical background, an FCM has been created that describes the causal
relationships among the parameters of the problem we are examining. The causal relationships,
which have arisen from existing knowledge and experience, illustrate the different aspects of the
functioning of smart entertainment devices and the way in which these concepts are feedback, either
in their interactions or in the overall dynamics of the system. To capture this knowledge, a directed
graph was created, with the nodes representing the variables of the problem. The main goal of this is
to predict the behavior of the system under the given recording conditions or not of the system
parameters. Learning has been gained through processes that are the result of continuous
interventions, including the modeled and specialists’ views until the system is balanced.
In simple terms, the written relationships of the map in Figure 4 outline the human experience
and knowledge to solve the problem as a result of continuous observation and intervention [29–30].
Figure 4. A depiction of the proposed FCM to ADVOCATE framework.
Specifically, FCM concepts like state, feature, concept, neuron, etc., represents knowledge and
related variables, states, events, inputs, and outputs in a way that is commensurate with that of
human beings. This methodology could support us to develop sophisticated systems, as it is
commonly accepted that the more fuzzy and symbolic representation is employed to model a system,
the more sophisticated the system is. The FCM concepts it emerged after exhaustive testing (trial and
error), taking into account the certainty, uncertainty, and risk of each scenario, while focusing on its
simplicity. Trial and error is characterized by repeated, varied attempts which are continued until
success. In order to find the best solution by the proposed method, we evaluate each trial model based
on the predefined set of criteria, the existence of which is a condition for the possibility of finding an
optimal solution.
The above procedure gave us the true inter-relationship among the problem’s parameters.
Specifically, after a thorough and detailed continuous study through trial and error, and after
Figure 4. A depiction of the proposed FCM to ADVOCATE framework.
Specifically, FCM concepts like state, feature, concept, neuron, etc., represents knowledge and
related variables, states, events, inputs, and outputs in a way that is commensurate with that of human
beings. This methodology could support us to develop sophisticated systems, as it is commonly
accepted that the more fuzzy and symbolic representation is employed to model a system, the more
sophisticated the system is. The FCM concepts it emerged after exhaustive testing (trial and error),
taking into account the certainty, uncertainty, and risk of each scenario, while focusing on its simplicity.
Trial and error is characterized by repeated, varied attempts which are continued until success. In order
to find the best solution by the proposed method, we evaluate each trial model based on the predefined
set of criteria, the existence of which is a condition for the possibility of finding an optimal solution.
The above procedure gave us the true inter-relationship among the problem’s parameters.
Specifically, after a thorough and detailed continuous study through trial and error, and after analogous

thresholds have been set for the expert opinion, it has been realized that in each use scenario, if at least
8 of the parameters to be tested are recorded, we have profiling. Also, there is an increased chance of
profiling, more than 95% of cases, when we record at least 3 parameters out of the following: Username,
Location_History, IP, Device_Number, and Interests.
This particular revelation of hidden knowledge about this problem has been transformed into the
following two rules for creating the classes of the problem, namely:
𝐶 and 𝐶 :
following rule:
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴 (1)
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴
( )
(2)
The class is Yes (profiling), if there are at least 8 parameters recorded, otherwise it is No
(no-profiling).
𝐶 and 𝐶 :
following rule:
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴 (1)
𝐴 = 𝑓 𝐴 + 𝑊
,
𝐴
( )
(2)
The class is Yes (profiling), if at least 3 parameters are recorded from the Username,
Location_History, IP, Device_Number and Interests, otherwise they are No (no-profiling).
The combination of the above 2 rules after being applied to the total of 4096 cases attributed 2134
classes Yes and 1962 No. These classes illustrate the problem of user profiling by recording data from
smart entertainment devices and can be used for intelligent training standards or algorithms that will
allow the immediate information of the user and optimal decision-making on the security of their
personal data and their protection against profiling.
Similarly, as stated above, in theory there are exponential multiple and different cases that can
be mapped as rules and that can give a different view of the problem. The methodology followed in
this paper is indicative and focuses on the simplicity of implementing a solid and valuable synthetic
dataset appropriate to train the learning algorithms used in this research. Table 2 presents the Table 1
after applying the above rules.
Table 2. Table 1 after applying the rules.
Username Sex Age
Location
_History
Device
_Number
Device
_Type
IP OS Automation
Average
_Time
Average
_Duration
Interests Class
1 1 1 1 0 1 0 1 1 1 0 0 Yes
1 1 1 0 1 1 0 1 1 0 0 0 No
1 1 0 1 0 1 0 1 1 0 0 0 No
1 0 0 1 1 1 1 0 0 0 0 0 Yes
Specifically, in the first case, we have the recording (1), i.e., 8 different parameters, so we
automatically have the case of profiling. In the second and third cases we have, respectively, 7 and
6 records (1), which do not fall under the second rule, i.e., 3 of the Username, Location_History
Device_Number, IP and Interests are not recorded. Finally, in the fourth case, although we have
the recording (1) of only 5 cases, recording of these four parameters (Username, Location_History,
Device_Number and IP) allows user profiling.
Once the problem classes have been created in the dataset modeling our problem, the identification
of conflicting rules that can lead to profiling is done using Extreme Learning Machines (ELMs) [31].
ELMs are Single-Hidden Layer Feed-Forward Neural Networks (SHLFFNNs), with N neurons at
hidden layer, randomly selected input weights, and random constant polarization values in the hidden
layer neurons, while weights at its output are calculated by a single multiplication of tables. SHLFFNNs
are used in ELMs because they can handle any continuous function and classify any non-continuous
areas, with the ability to accurately read K samples, while their learning speed can be even thousands
of times greater than the speed of Conventional Neural Networks (CNNs) which are trained using the
Back-Propagation method. This characteristic is due to the general view, which is demonstrated in
the theoretical background of ELMs, that SHLFFNN’s hidden layer (feature mapping) is not required
to work in a coordinated fashion, hidden neurons may have been created randomly and all network
parameters are independent of activation functions and training data. It is also noteworthy that ELMs
can handle non-differential activation equations and do not address known neural network problems
such as the definition of the appropriate stopping criterion, the learning rate, and learning epochs.
In ELMs, the weights of input level W and biases β are randomly set and not adjusted. Because
the input weights are fixed, the output weights b are independent of them, as opposed to the

Back-Propagation method, so that to produce an immediate solution without repetition. Essentially,
random weights at the input level improve the generalization properties of a linear system because
they produce weakly related properties at the hidden layer. Given that the output of a linear system is
always correlated with the input data, if the weight range of the solution is limited, the rectangular
inputs provide a wider range of solutions than those supported by the weights. Also, small variations in
weights allow the system to become more stable and noise-resistant as input errors will not be amplified
at the output of a linear system with little correlation between input and output weights. Thus,
the random ranking of weights, which produces loosely coupled characteristics at the hidden layer,
allows for a satisfactory solution and good generalization performance to be achieved. Corresponding
to a linear output level, the Generalized Least Squares Approximation [32] approach is used, where such
a solution is also linear and very fast to calculate.
Thus, the random weighting, which produces weakly correlated features at the hidden layer,
allows for a satisfactory solution and a good generalization performance. Respectively, for a linear
output layer, the Generalized Least Squares Approximation approach is used, where such a solution is
also linear and very fast to calculate.
Specifically, in an ELM using SHLFFNN and random representation of the hidden layer neurons,
the input data are mapped to a random L-dimensional space with a discrete set of training N,
where (xi, ti), i ∈ 1, N µε xi ∈ Rd and ti ∈ Rc. The output of the network can be represented as
follows [29]:
fL(x) =
L
i=1
βihi(x) = h(x)β i ∈ 1, N (6)
where β = [β1, . . . , βL]T
is the output of the weights matrix between the hidden nodes and the output
nodes, h(x) = [g1(x), . . . , gL(x)] are the exits of hidden nodes (random hidden attributes) for input x,
and g1(x) is the output of the hidden node i. Based on dataset N (xi, ti) N
i=1, an ELM can solve the
learning problem Hβ = T, where T = [t1, . . . , tN]T
are the target tags and H the output panel of the
hidden layer shown below:
H ωj, bj, xi =


g(ω1x1 + b1) · · · g(ωlx1 + bl)
...
...
...
g(ω1xN + b1) · · · g(ωlxN + bl)


N×l
(7)
Prior to the training, the input weights table ω and the biases β are randomly generated in the
space [−1, 1], with ωj = ωj1, ωj2, . . . , ωjm
T
and βj = βj1, βj2, . . . , βjm
T
. The output weights table
of the hidden level H is calculated by the activation function and the training data based on the
following function:
H = g(ωx + b) (8)
The output weights B can be calculated from the relation:
β =
C
+ HT
H
−1
HT
X (9)
where H = [h1, . . . , hN] are the outputs of the hidden layer and X = [x1, . . . , xN] are the input data.
B can also be calculated from the general relation:
β = H+
T (10)
where H+ is the Moore–Penrose generalized inverse matrix for matrix H.
The hidden layer is responsible for transforming the input data into a different representation.
The transformation is a two-stage process:

1. Input data enters the hidden layer, using weights and corresponding biases of the input layer.
2. The data is transformed based on a non-linear transformation function.
The hidden layer, and in particular, the second stage of input data transformation, is the only point
where a non-linear process is used throughout the method, which significantly increases the learning
abilities of ELMs. After the transformation, the data in the hidden layer are used to find the weights
of the output layer. The construction of hidden-layer neurons in a random manner is an ingenious
architectural technique for fast learning, which essentially eliminates the problem of overfitting.
At the hidden layer there is no explicit constraint and various transformation functions can be
used, such as sigmoidal, tangential, and threshold. Nonetheless, there are some linear neurons that do
not need to use any transformation function as they are capable of learning correlations between the
input data and the target data directly, without being approximated by a nonlinear function. Usually
in the case of linear neurons, these neurons are equal the number of input data, at a 1 to 1 match.
The exceptional characteristics of ELMs [30], such as efficiency, speed, and optimal generalization
capabilities, have been shown to have a wide range of problems from different disciplines, with often
comparable or even better results than those of deep learning algorithms. Another important fact that
enhances the use of ELMs is that they work best when the input patterns are from the same boundary
distribution or follow a common cluster structure, as in the case we are examining.
6. Results
In the case of multi-class classifiers, the full probability density of both classes should be known
for estimating the actual error during training. The calculation of the classification performance is
calculated by constructing a Confusion Matrix (CM), where the numbers of misclassifications are
related to the False Positive (FP), False Negative (FN), True Positive (TP), and True Negative (TN).
The Total Accuracy (TA) is defined by using the equation [33]:
TA =
TP + TN
N
(11)
Equations (12), (13), and (14) below, define the Precision (PRE), the Recall (REC), and the F-Score
indices, respectively:
PRE =
TP
TP + FP
(12)
REC =
TP
TP + FN
(13)
F − Score = 2·
PRE · REC
PRE + REC
(14)
The Root Mean Square Error (RMSE) is defined by using the equation:
RMSE =
1
n
n
j=1
P(ij) − Tj
2
(15)
Also, a Receiver Operating Characteristic (ROC) area is a performance metric of all
classification thresholds.
Table 3 presents the results of the method proposed in this study, as well as the results of the
corresponding methods tested in the dataset under consideration.

Table 3. Algorithm comparison providing classification accuracy and performance metrics.
Classifier TA RMSE PRE REC F-Score ROC Area Training Time Validation
ELM 99.87% 0.0353 0.999 0.999 0.999 0.999 1.1 s 10-FCV
SVM 97.92% 0.1441 0.980 0.979 0.979 0.980 14.9 s 10-FCV
k-NN 97.53% 0.2306 0.976 0.975 0.975 0.994 15.3 s 10-FCV
Random Forest 98.17% 0.1424 0.982 0.982 0.982 0.996 15.6 s 10-FCV
As can be seen, the use of ELM creates a robust learning system that effectively solves a realistic,
multifactorial, and highly complex problem. The choice of the proposed model is significantly superior
to the algorithms compared, both in categorization metrics and in training time, where ELM is almost
15 times faster than the other models. This feature is a clear demonstration of the potential of the
proposed method, taking into account the difficulties of the research environment.
In conclusion, it should be emphasized that ELM implements, in the most realistic way, the process
of identifying conflicting rules that can lead to profiling, as the training of the algorithm was based on
a highly specialized initial set of training, but in which the proposed system managed to generalize
and respond optimally to unspecified situations.
It is also important to highlight that the stability of the ELM and the clear identification it
offers in the modeling of complex systems offered by FCMs advocate the adoption of a method that
delivers high-precision results, capable of responding to the problem, as well as in cases of similar
complex situations.
7. Conclusions
An innovative, reliable, low-demand, and highly effective system for detecting conflicting
consents and user consent policies regarding personal data, based on sophisticated computational
methods, was presented in this work. The Intelligent Policies Analysis Mechanism (IPAM) implements
sophisticated conflict recognition rules to assist the average user in making an optimal decision and
protecting them from impending profiling, with computational intelligence methods. Specifically,
using Fuzzy Cognitive Maps (FCMs), which is an advanced form of neuro-fuzzy decision support
system, the protection of user privacy in the IoT ecosystem is modeled in the most efficient and
intelligent way. Respectively, with the use of Extreme Learning Machines (ELMs), which are highly
efficient neural systems of small and flexible architecture, which can operate optimally in complex
environments, it is possible to identify the conflicting rules that may result in profiling.
This proposed mechanism, which is an integral part of the ADVOCATE framework [12–14],
greatly enhances its security mechanisms and is a promising intelligent mechanism for protecting the
privacy of users whose personal data are the main target of modern cyberattack methods.
It is important to note that the application of artificial intelligence to methods of controlling and
protecting users’ personal data significantly enhances the active security mechanisms of these methods
and creates new perspectives on how to deal with cybercrime. It is also important to emphasize that
the complexity of the IoT ecosystem, the uncertainty it brings, as well as the instability of other learning
algorithms in such a dynamic environment, favor the adoption of a method that normalizes the noisy
field and brings consistent results capable of modeling serious, multi-dimensional problems.
On the other hand, literature points out how the side-channel information can be used to extract
some of the sensitive data that the GDPR tries to protect [34]. For example, Palmieri et al. [35]
present a private routing protocol for anonymous communication between different networks (e.g.,
wireless sensor networks, etc.) using technologies such as Spatial Bloom Filters (SBF), tunneling,
and homomorphic encryption. The proposed routing protocol preserves context privacy and prevents
adversaries from discovering the network topology and structure, as routing information is encrypted
and computed by performing calculations in the actual encrypted data. Consequently, the achieved
privacy is vital in preventing adversaries from obtaining valuable network information from a successful
attack on a single node of the network and reduces the likelihood of an escalation attack.

Future study could include additional behavior analysis of the smart entertainment devices,
in order to be improved by additional adjusting of the parameters of the suggested framework, so that
an even more effective, precise, and faster classification process could be reached. Multi-format
exemplifications may support a reporting system as part of a general decision framework. In addition,
more sophisticated methods could be used for precise identification of contradictory and conflicting
policies. Also, it would be essential to study the expansion of this method by applying the same
architecture in a big data architecture framework like Hadoop [36]. In conclusion, an additional
component that could be considered in the way of future development concerns the process of the
proposed framework with methods of self-adaptive improvement in order to fully automate the IPAM
in contrast to a personal data breach.
Author Contributions: Conceptualization, K.D., K.R., G.D.; investigation, K.D., K.R., G.D.; writing—original
draft preparation, K.D., K.R.; writing—review and editing, K.D., K.R., G.D. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments: We would like to show our gratitude to Gonzalo Napoles Ruiz, at Tilburg University for
sharing his FCM Expert software and for his comments on an early draft of this manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem

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A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem