Privacy-Preserving Truth Discovery for Crowdsensing with Configurable Reliability

Crowdsensing in edge computing leverages the low-latency and high-efficiency communications to conduct large-scale data sensing and truth discovery at a reduced cost. Privacy-preserving truth discovery (PPTD) has emerged as a promising technique for securely inferring truthful information from noisy, incomplete, or conflicting data from multiple sources. However, designing a secure and reliable PPTD for edge-based crowdsensing meets challenges due to the heterogeneous nature of the edge computing environment, which renders edge nodes susceptible to breakdown and collusion. Existing PPTDs lack simultaneous consideration of this dropout problem and collusion attacks. This study tackles the challenges by proposing a PPTD scheme for edge-based crowdsensing with configurable reliability, which enhances security with respect to collusion attacks and dropout problems in the meantime. The scheme treats edge nodes with various reliability levels and offers a flexible way to configure their reliability. First, vector space secret sharing is employed as a fundamental building block to construct methods for secure multi-party computation. Then, protocols for PPTD involving workers and edge nodes are devised. A formal analysis is conducted for the security and performance, followed by experiments showing its effectiveness.