Title
A Differential Private Mechanism to Protect Trajectory Privacy in Mobile Crowd-Sensing
Abstract
With the fast development of smart mobile devices, the mobile crowd-sensing (MCS) has been witnessed as a new data collection paradigm. In this paper, we consider a scenario that an MCS server tries to collect trajectories from participants. In order to protect the participants' location privacy from their own side, we let participants submit noisy data to the server. In addition, we assume that the data collection is delay tolerant which means each participant is allowed to submit his trajectory in a bundle instead of submitting locations one by one. Based on this assumption, we regard each trajectory as a vector in the high dimension space and design a trajectory protection algorithm to perturb the true trajectory before submission. We use the differential privacy (DP) as the privacy model so we can estimate the amount of noise given a privacy level. To evaluate our mechanism, we use real world traffic data collected from Shanghai taxis and compare it with existing work. The results show that our mechanism not only guarantees privacy protection, but also preserves trajectories' utility.
Year
DOI
Venue
2019
10.1109/WCNC.2019.8885628
2019 IEEE Wireless Communications and Networking Conference (WCNC)
Keywords
Field
DocType
Trajectory privacy,Local differential privacy,Mobile crowd-sensing
Data collection,Noisy data,Differential privacy,Computer science,Server,Taxis,Computer network,Mobile device,Bundle,Trajectory
Conference
ISSN
ISBN
Citations 
1525-3511
978-1-5386-7647-9
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Hongyu Huang127721.83
Xin Niu25611.39
Chao Chen312.07
Chunqiang Hu440731.79