Privacy-Friendly Decentralized Data Aggregation For Mobile Crowdsensing

Xudong Wang, Chenhao Ying, Yuan Luo

Abstract: In recent years, crowdsensing has received extensive attention both in academia and industry. However, most of the prior works are based on centralized framework, where the users upload the sensor data to a platform alone. In this paper, we propose a decentralized data aggregation algorithm for crowdsensing, in which, participants negotiate the average of their sensor data in a pure distributed manner while enjoying differential privacy guarantee. Taking participants' privacy into consideration, we first redesign a distributed averaging algorithm by artificially adding random noise to accommodate the mobile crowdsensing scenario, where the sensor data may be sensible. We prove that, though random noises are involved, our algorithm almost surely yields an unbiased estimate of the exact average. We further theoretically formulate the trade-offs between differential privacy and the accuracy of the algorithm. Finally, we give the optimal choice for the additive noise with respect to the variance minimization of the estimate. The extensive simulations and realword test demonstrate the effectiveness of our algorithm.