Personalized Location Privacy Trading in Double Auction for Mobile Crowdsensing

Mobile crowdsensing systems (MCSs) are widely used in data collection due to their flexible deployment and comprehensive coverage in many IoT scenarios (e.g., road condition monitoring). Recently, the difference between workers' perception on location privacy has drawn researchers' attention. The only privacy trading mechanism in MCSs has been designed, however, in a single auction and single-minded way. Realizing task requesters' competition requirement and workers' task preference variance, in this article, we are the first to propose a double MCS auction mechanism with a personalized location privacy incentive. Specifically, this article introduces the concept of privacy budget, allowing workers to decide how much location information to disclose to the platform to realize personalized location privacy protection. Besides, considering the heterogeneity of sensing tasks and the diversity of task selection, each worker is allowed to offer several bids for interested tasks and to perform a subset of tasks in a bid if wins. In addition, our auction mechanism enables the platform to select winning requesters and workers and achieve ideal sensing service accuracy. Extensive theoretical analysis and experiment results validate that the proposed mechanism satisfies budget balance, individual rationality, and 2-D-truthfulness.

Automated summary

This article proposes a double MCS auction mechanism with a personalized location privacy incentive, which can meet the competition requirement of task requesters and the task preference variance of workers. The mechanism introduces the concept of privacy budget, allowing workers to decide how much location information to disclose to the platform for personalized location privacy protection. In addition, each worker can offer multiple bids for interested tasks and perform a subset of tasks in a bid if wins. Experimental results validate that the mechanism satisfies budget balance, individual rationality, and 2-D-truthfulness.