A Real-Time Data Collection Mechanism With Trajectory Privacy in Mobile Crowd-Sensing
Xin Niu, Hongyu Huang, Yantao Li
Abstract
As a new paradigm to serve and sense the intelligent city, mobile crowd-sensing (MCS) usually requires participants' real-time locations. However, uploading participants' true locations to servers or third parties raises privacy concerns. In this letter, we propose a real-time data collection mechanism with trajectory privacy (RDCTP) in MCS, which achieves w-event a-differential privacy for the crowd-sensing participants. Different from existing works, we focus on protecting the privacy of trajectories instead of individual locations. Specifically, RDCTP provides a-differential privacy for each sub-trajectory which consists of successive w locations. To achieve this, a participant first allocates the trajectory privacy budget to each location. Then, he perturbs his true location and gets candidate location set which satisfies a-differential privacy. Last, he submits a location from the set by solving an optimization problem that aims to tradeoff between the privacy and utility. We utilize real world traffic trajectories of Shanghai taxis to evaluate the RDCTP, and the results show that it not only protects participants' privacy, but also preserves the server's utility.