Achieving efficient and privacy-preserving truth discovery in crowd sensing systems

Benefiting from the development of network and mobile communication technologies, crowd sensing systems have emerged as new technology to sense and collect data via mobile devices. However, aggregated results of the collected data may not be accurate since information provided by devices may not be reliable. To tackle this problem, approaches based on truth discovery have been proposed to improve accuracy of data aggregation operations in crowd sensing systems. Nevertheless, it should be noted that most existing truth discovery proposals failed to consider user's data privacy. In this paper, we propose an Efficient and Privacy-preserving Truth Discovery (EPTD) scheme in crowd sensing systems. Specifically, we utilize the additive homomorphic privacy-preserving data aggregation and super-increasing sequence techniques to achieve both high performance and strong privacy protection. Security analysis indicates that the EPTD can achieve confidentiality of observed values and privacy protection of users' weights. Furthermore, extensive experiments demonstrate that the EPTD is better than existing proposals in terms of communication and computation overhead.