Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification
Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more ch...
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| Veröffentlicht in: | Soft computing (Berlin, Germany) Jg. 21; H. 21; S. 6297 - 6315 |
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| Sprache: | Englisch |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2017
Springer Nature B.V |
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. |
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| AbstractList | Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. |
| Author | D’Angelo, Gianni Rampone, Salvatore Palmieri, Francesco |
| Author_xml | – sequence: 1 givenname: Gianni surname: D’Angelo fullname: D’Angelo, Gianni email: dangelo@unisannio.it organization: Department of Science and Technology, University of Sannio – sequence: 2 givenname: Salvatore surname: Rampone fullname: Rampone, Salvatore organization: Department of Science and Technology, University of Sannio – sequence: 3 givenname: Francesco surname: Palmieri fullname: Palmieri, Francesco organization: Department of Computer Science, University of Salerno |
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| Keywords | Pervasive computing Apriori algorithm Naïve Bayes classifier Artificial intelligence Trust model |
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| SubjectTerms | Access control Algorithms Artificial Intelligence Behavior Computational Intelligence Computer architecture Computers Control Data mining Decision making Engineering Explicit knowledge Machine learning Mathematical Logic and Foundations Mechatronics Methodologies and Application Personal information Privacy Robotics Trust Trustworthiness Ubiquitous computing Wireless communications |
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| Title | Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification |
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