A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/4/2/9 |
| _version_ | 1797569467451965440 |
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| author | Konstantinos Demertzis Konstantinos Rantos George Drosatos |
| author_facet | Konstantinos Demertzis Konstantinos Rantos George Drosatos |
| author_sort | Konstantinos Demertzis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:12:02Z |
| format | Article |
| id | doaj.art-0add369ef735463a82dd071171637729 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T20:12:02Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-0add369ef735463a82dd0711716377292023-11-19T22:49:59ZengMDPI AGBig Data and Cognitive Computing2504-22892020-04-0142910.3390/bdcc4020009A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT EcosystemKonstantinos Demertzis0Konstantinos Rantos1George Drosatos2Department of Computer Science, International Hellenic University, 65404 Kavala, GreeceDepartment of Computer Science, International Hellenic University, 65404 Kavala, GreeceInstitute for Language and Speech Processing, Athena Research Centre, 67100 Xanthi, GreeceThe 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.https://www.mdpi.com/2504-2289/4/2/9Internet of ThingsprivacyGDPRdigital consents managementfuzzy cognitive mapextreme learning machine |
| spellingShingle | Konstantinos Demertzis Konstantinos Rantos George Drosatos A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem Big Data and Cognitive Computing Internet of Things privacy GDPR digital consents management fuzzy cognitive map extreme learning machine |
| title | A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem |
| title_full | A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem |
| title_fullStr | A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem |
| title_full_unstemmed | A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem |
| title_short | A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem |
| title_sort | dynamic intelligent policies analysis mechanism for personal data processing in the iot ecosystem |
| topic | Internet of Things privacy GDPR digital consents management fuzzy cognitive map extreme learning machine |
| url | https://www.mdpi.com/2504-2289/4/2/9 |
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