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Jayasinghe, U, Otebolaku, AM, Um, TW and Lee, GM (2018) Data centric trust evaluation and predication framework for IoT. In: 2017 ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K) . (ITU Kaleidoscope 2017, 27 November 2017 - 29 November 2017, Nanjing, China).
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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) |
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| Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Uncontrolled Keywords: | Data Trust; Knowledge; Reputation; Experience; Collaborative Filtering; Ensemble Learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IEEE and ITU |
| Date Deposited: | 05 Oct 2017 11:26 |
| Last Modified: | 30 May 2024 10:14 |
| DOI or ID number: | 10.23919/ITU-WT.2017.8246999 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/7291 |
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