Catch You If You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems

Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. That is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy" cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, we propose V-PATD, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems. In V-PATD, a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server. Since most of the computation burdens are carried by the cloud server, our verification approach is efficient and scalable. Moreover, users' data is perturbed with the principles of local differential privacy. Security analysis shows that the proposed perturbation mechanism guarantees a high aggregation accuracy even if large noises are added. Compared to existing solutions, extensive experiments conducted on real crowdsensing systems demonstrate the superior performance of V-PATD in terms of accuracy, computation and communication overheads.