Learning the Truth Privately and Confidently: Encrypted Confidence-Aware Truth Discovery in Mobile Crowdsensing

Mobile crowdsensing enables convenient sensory data collection from a large number of mobile devices and has found various applications. In the real practice, however, the sensory data collected from various mobile devices are usually unreliable. To extract truthful information from the unreliable sensory data in mobile crowdsensing, the topic of truth discovery has received wide attention recently, which essentially operates by estimating user reliability degrees and performing reliability-aware truthful aggregation. Despite the effectiveness, applying truth discovery in mobile crowdsensing faces several privacy and security challenges. First, the sensory data and reliability degrees of users may reveal privacy-sensitive information and, thus, demand strong protection. Second, the requester that initiates a crowdsensing application usually needs to have monetary investment, so the inferred truths can be the requester's proprietary information and should be protected as well. In this paper, we propose a new system architecture enabling encrypted truth discovery in mobile crowdsensing. We focus on general and realistic mobile crowdsensing scenarios with varying levels of user participation, and our security design is built on the confidence-aware truth discovery (CATD) approach for its state-of-the-art accuracy in such scenarios. In our system architecture, users send encrypted sensory data to the cloud, where CATD is then conducted in the encrypted domain. The final encrypted inferred truths are sent to the requester for decryption. Along the whole workflow, the sensory data and reliability degrees of users, as well as the inferred truths of the requester, are kept private. Extensive experiments over real-world mobile crowdsensing dataset show that our design achieves practical performance on mobile devices.