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

1
Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
2
Institute for Language and Speech Processing, Athena Research Centre, 67100 Xanthi, Greece
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2020, 4(2), 9; https://doi.org/10.3390/bdcc4020009
Received: 21 March 2020 / Revised: 15 April 2020 / Accepted: 20 April 2020 / Published: 27 April 2020
(This article belongs to the Special Issue Advanced Data Mining Techniques for IoT and Big Data)
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. View Full-Text
Keywords: Internet of Things; privacy; GDPR; digital consents management; fuzzy cognitive map; extreme learning machine; feed-forward neural network Internet of Things; privacy; GDPR; digital consents management; fuzzy cognitive map; extreme learning machine; feed-forward neural network
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MDPI and ACS Style

Demertzis, K.; Rantos, K.; Drosatos, G. A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem. Big Data Cogn. Comput. 2020, 4, 9. https://doi.org/10.3390/bdcc4020009

AMA Style

Demertzis K, Rantos K, Drosatos G. A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem. Big Data and Cognitive Computing. 2020; 4(2):9. https://doi.org/10.3390/bdcc4020009

Chicago/Turabian Style

Demertzis, Konstantinos; Rantos, Konstantinos; Drosatos, George. 2020. "A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem" Big Data Cogn. Comput. 4, no. 2: 9. https://doi.org/10.3390/bdcc4020009

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