Releasing Correlated Trajectories: Towards High Utility and Optimal Differential Privacy

Ou, Lu; Qin, Zheng; Liao, Shaolin; Hong, Yuan; Jia, Xiaohua

doi:10.1109/tdsc.2018.2853105

Releasing Correlated Trajectories: Towards High Utility and Optimal Differential Privacy

Journal Article · Tue Jul 10 00:00:00 EDT 2018 · IEEE Transactions on Dependable and Secure Computing

DOI:https://doi.org/10.1109/tdsc.2018.2853105· OSTI ID:1804098

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Hunan Univeristy, Changsha (China)
Argonne National Lab. (ANL), Argonne, IL (United States); Illinois Institute of Technology, Chicago, IL (United States)
Illinois Institute of Technology, Chicago, IL (United States)
City Univ. of Hong Kong (Hong Kong)

A mutual correlation between trajectories of two users is very helpful to real-life applications such as product recommendation and social media. While providing tremendous benefits, the releasing of correlated trajectories may leak sensitive social relations, due to potential links between mutual correlations and social relations. To the best of our knowledge, here we take the first step to propose a mathematically rigorous n-body Laplace framework, satisfying epsilon-differential privacy, which efficiently prevents a social relation inference through the mutual correlation between n-node trajectories of two users. The problem is mathematically formulated by defining a trajectory correlation score to measure the social relation between two users. Then, under the n-body Laplace framework, we propose two Lagrange Multiplier-based Differentially Private (LMDP) approaches to optimize the privacy budgets, for the data utility measured by location distances and the data utility measured by location correlations, i.e., UD-LMDP and UC-LMDP. Also, we present detailed analyses of privacy, data utility, adversary knowledge and the constrained optimizations. Finally, we perform experimental studies with real-life data. Our experimental results show that our proposed approaches achieve better privacy and data utility than the existing approaches.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE; National Science Foundation of China; National Science Foundation (NSF); Key Project of National Science Foundation of China; Science and Technology Key Projects of Hunan Province

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1804098

Journal Information:: IEEE Transactions on Dependable and Secure Computing, Journal Name: IEEE Transactions on Dependable and Secure Computing Journal Issue: 5 Vol. 17; ISSN 1545-5971

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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97 MATHEMATICS AND COMPUTING
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