Privacy-Preserving and Reliable Truth Discovery for Heterogeneous Fog-Based Crowdsensing

Yihuai Liang, Yan Li, Byeong-Seok Shin

Abstract: Truth discovery is an effective technique for resolving data conflicts in crowdsensing. Fog-based mobile crowdsensing utilizes low-latency and high-efficiency communications capabilities of fog computing to achieve large-scale data sensing at a low cost. Privacy-preserving truth discovery (PPTD) has garnered significant attention in recent years due to the inclusion of users’ sensitive information in sensory data. However, existing PPTDs have not adequately addressed fog servers’ reliability and mobile devices’ efficiency simultaneously. Challenges are that fog servers are susceptible to breakdowns and collusion that causes privacy breaches, while mobile devices have limited resources. We thus propose a reliable and efficient PPTD for fog-based crowdsensing. We employ a threshold secret sharing scheme to establish secure multi-party computation primitives. These primitives are then used to construct an arithmetic circuit–an essential component of the PPTD. This approach preserves privacy of sensory data, as well as intermediate and final results, while accounting for server collusion, dropout, and mobile devices’ efficiency. It has $T$-out-of-$N$ threshold reliability that resists ($T-1$) servers’ collusion and ($N-T$) servers’ dropout. Experimental results demonstrate that our scheme reduces worker processing time by at least one order of magnitude and network communication overhead by approximately two orders of magnitude compared to existing PPTD methods.

External IDs:doi:10.1109/tdsc.2024.3497965