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Data centric trust evaluation and predication framework for IoT

Jayasinghe, U and Otebolaku, AM and Um, TW and Lee, GM Data centric trust evaluation and predication framework for IoT. In: ITU Kaleidoscope 2017, 27 November 2017 - 29 November 2017, Nanjing, China. (Accepted)

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Abstract

Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Data Trust; Knowledge; Reputation; Experience; Collaborative Filtering; Ensemble Learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: IEEE and ITU
Date Deposited: 05 Oct 2017 11:26
Last Modified: 05 Oct 2017 11:26
URI: http://researchonline.ljmu.ac.uk/id/eprint/7291

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