Lightweight Privacy-Preserving and Fault-Tolerant Truth Discovery for Mobile Crowdsensing Systems

As a paradigm for encouraging users to contribute data spontaneously, mobile crowdsensing (MCS) has received considerable attention recently. It is crucial to evaluate the truthfulness of MCS data by proper truth discovery mechanisms. Although recent truth discovery schemes can determine truthful information, they either provide limited privacy preservation or have heavy computation and communication overheads. Moreover, most of them are not resilient to malicious faults and active attacks. To tackle the above problems, we propose two fault-tolerant and privacy-preserving truth discovery solutions. Our first scheme is mainly used for scenarios with a relatively stable number of users, where participants do not frequently join or leaves. Integrating ring signature with the perturbation technique, we design an anonymous and privacy-preserving truth discovery scheme, namely RsAnonTD, which can achieve privacy preservation and resist active attacks. To address the challenge with dynamically changed workers, we devise a multi-client inner product functional encryption scheme with a lightweight zero-knowledge proof protocol (namely McFeKDeTD) for defending against active attacks. The security analysis shows that both schemes can preserve the privacy of sensory data, weights, and estimated truths while resisting active attacks, thereby guaranteeing fault tolerance. Extensive experiments demonstrate that our designs achieve superior performance than other schemes in terms of accuracy, convergence speed, and system overheads. For example, compared with the state-of-the-art approach RPTD-II, which has a security level comparable to ours, our proposed schemes, RsAnonTD and McFeKDeTD, reduce the computational overheads approximately by 98% and 69%, respectively.