Incentivizing Crowdsensing-Based Noise Monitoring with Differentially-Private Locations

Abstract

Mobile crowd sensing is a technique where a crowd sensing server outsources sensing tasks to the crowd for mobile data collection. In mobile crowd sensing, some tasks require location information to achieve their objectives, such as road monitoring, indoor floor plan reconstruction, and smart transportation. This required information incurs severe concerns on location privacy leakage and threatens workers' properties as well as public safety. In some cases, even sensing data itself can be used as auxiliary information resulting in location privacy breaches. Many existing works apply differential privacy mechanisms for location privacy preservation to tackle this problem, but they cannot efficiently fulfill privacy goals because each worker only considers his own privacy. As a consequence, the accumulated privacy budget will lower down the composed privacy level of all the workers' locations. In addition, deploying differential privacy is costly for workers and it will degrade the quality of data required in crowd sensing tasks. How to balance the cost and provide accurate aggregated data while fulfilling privacy objectives becomes a challenging issue. In this paper, we propose a group-differentially-private game-theoretical solution, which addresses these limitations in a privacy-preserving and efficient way. Our scheme enables the indistinguishability of workers' locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks. The effectiveness and efficiency of our scheme are thoroughly evaluated based on real-world datasets.