Location Privacy-Preserving Truth Discovery in Mobile Crowd Sensing

Abstract

Truth discovery techniques are commonly used in mobile crowd sensing (MCS) applications to infer accurate aggregated results based on quality-aware data aggregation. However, the location information of participants may be exposed when they upload their sensitive geo-tagged sensory data to relative platforms. While there are considerable existing privacy preserving truth discovery schemes for MCS, they mainly focus on protecting the privacy of sensory data, neglecting the tagged location information which is of equal if not higher importance for the privacy of participants. In this paper, we propose a novel and efficient location privacy preserving truth discovery (LoPPTD) mechanism, which can achieve data aggregation with high accuracy, while protecting both location privacy and data privacy of users. By structuring multi-dimensional sensory data obtained at different locations and exploiting homomorphic Paillier encryption, our approach can prevent leakage of both sensory data and tagged locations effectively. Also, super-increasing sequence techniques are employed in Lo-PPTD to ensure efficiency and feasibility. Theoretical analysis and thorough experiments performed on real-world datasets demonstrate that the proposed scheme can achieve high aggregation accuracy while providing complete privacy protection for users.