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Soft Computing
Published in: Soft Computing 21/2017

14-05-2016 | Methodologies and Application

Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification

Authors: Gianni D’Angelo, Salvatore Rampone, Francesco Palmieri

Published in: Soft Computing | Issue 21/2017

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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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Metadata
Title
Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification
Authors
Gianni D’Angelo
Salvatore Rampone
Francesco Palmieri
Publication date
14-05-2016
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 21/2017
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2183-1

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