Participant Selection Strategy With Privacy Protection for Internet of Things Search

Abstract

Nowadays, with the rapid developments of the Internet of Things, the information on physical entities has shown explosive growth. The emergence of Internet of Things search technology has facilitated the storage, organization, and management of massive entity information. Aiming at the issues of security and privacy and the target to obtain the optimal perceiving results by selecting participants to ensure the quality of service of the IoT platform, this paper proposes a participant selection strategy with privacy protection for IoT search (PSSPP). Firstly, the search task requester and the task contents are anonymized and blinded respectively, and then individual attributes of participants are mapped to hide the identity information of participants. Secondly, in order to choose the participants who meet the task requirements, the participants are preliminarily selected based on the bloom filter. Finally, the perceiving trust value and relative credibility of participants in participating tasks are dynamically evaluated. Simulation results demonstrate that the proposed scheme can reduce the attack probability of malicious users toward task requesters and participants, and rationally select the appropriate task participants.

Keywords

• IoT search
• mobile crowd sensing
• participant selection
• privacy-preserving
• bloom filter