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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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 21; no. 21; pp. 6297 - 6315
Main Authors: D’Angelo, Gianni, Rampone, Salvatore, Palmieri, Francesco
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2017
Springer Nature B.V
Subjects:
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
Pervasive computing
Apriori algorithm
Naïve Bayes classifier
Artificial intelligence
Trust model
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary: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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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-016-2183-1