Privacy-preserving Truth Discovery with Outlier Detection in Mobile Crowdsensing Systems

Recently, there have been many discussions in mobile crowd-sensing about privacy-preserving truth discovery because of its ability to extract truthful information from noisy or biased sensory data without privacy breaches. However, in practical applications, users (referred to as workers) may report outliers due to device malfunction, malicious workers, etc. These outliers will dramatically impact the accuracy of the truth discovery result. Detecting outliers based on existing privacy preservation schemes will carry an intolerable overhead, dramatically reducing the system's availability. In this paper, we propose our privacy-preserving truth discovery scheme that can detect outliers. Specifically, we adopt an anonymous mechanism to achieve privacy preservation. Since the existing anonymous mechanisms require huge overhead and do not work correctly when some workers exit, they are difficult to be applied in mobile crowdsensing systems. We design a lightweight and robust anonymous mechanism based on the edge computing paradigm. In addition, we eliminate the impact of outliers through outlier detection to achieve robustness of truth discovery results. Finally, we demonstrate the security of our scheme through security analysis and the efficiency of our scheme in terms of computation and communication overhead through extensive experiments.