NDLI: Privacy-Preserving Compressive Sensing for Crowdsensing Based Trajectory Recovery

Content Provider	IEEE Xplore Digital Library
Author	Linghe Kong Liang He Xiao-Yang Liu Yu Gu Min-You Wu Xue Liu
Copyright Year	2015
Description	Author affiliation: Shanghai Jiao Tong Univ., Shanghai, China (Xiao-Yang Liu; Min-You Wu) \|\| McGill Univ., Montreal, QC, Canada (Linghe Kong; Xue Liu) \|\| Univ. of Michigan, Ann Arbor, MI, USA (Liang He) \|\| IBM Res., Austin, TX, USA (Yu Gu)
Abstract	Location based services have experienced an explosive growth and evolved from utilizing a single location to the whole trajectory. Due to the hardware and energy constraints, there are usually many missing data within a trajectory. In order to accurately recover the complete trajectory, crowdsensing provides a promising method. This method resorts to the correlation among multiple users' trajectories and the advanced compressive sensing technique, which significantly outperforms conventional interpolation methods on accuracy. However, as trajectories exposes users' daily activities, the privacy issue is a major concern in crowdsensing. While existing solutions independently tackle the accurate trajectory recovery and privacy issues, yet no single design is able to address these two challenges simultaneously. Therefore in this paper, we propose a novel Privacy Preserving Compressive Sensing (PPCS) scheme, which encrypts a trajectory with several other trajectories while maintaining the homomorphic obfuscation property for compressive sensing. Under PPCS, adversaries can only capture the encrypted data, so the user privacy is preserved. Furthermore, the homomorphic obfuscation property guarantees that the recovery accuracy of PPCS is comparable to the state-of-the-art compressive sensing design. Based on two publicly available traces with numerous users and long durations, we conduct extensive simulations to evaluate PPCS. The results demonstrate that PPCS achieves a high accuracy of <;53 m and a large distortion between the encrypted and the original trajectories (a commonly adopted metric of privacy strength) of >9,000 m even when up to 50% original data are missing.
Starting Page	31
Ending Page	40
File Size	526289
Page Count	10
File Format	PDF
ISBN	9781467372145
ISSN	10636927
DOI	10.1109/ICDCS.2015.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Accuracy Servers Privacy Compressed sensing Encryption
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

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