An Optimal Pufferfish Privacy Mechanism for Temporally Correlated Trajectories

Ou, Lu; Qin, Zheng; Liao, Shaolin; Yin, Hui; Jia, Xiaohua

doi:10.1109/ACCESS.2018.2847720

Title: An Optimal Pufferfish Privacy Mechanism for Temporally Correlated Trajectories

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Abstract

Temporally correlated trajectories are ubiquitous, and it has been a challenging problem to protect the temporal correlation from being used against users’ privacy. In this work, we propose an optimal Pufferfish privacy mechanism to achieve better data utility while providing guaranteed privacy of temporally correlated daily trajectories. First, a Laplace noise mechanism is realized through geometric sum of noisy Fourier coefficients of temporally correlated daily trajectories. Then, we prove that the proposed noisy Fourier coefficients’ geometric sum satisfies Pufferfish privacy, i.e. , the so-called FGS-Pufferfish privacy mechanism. Furthermore, we achieve better data utility for a given privacy budget by solving a constrained optimization problem of the noisy Fourier coefficients via the Lagrange multiplier method. What is more, a rigorous mathematical formula has been obtained for the Fourier coefficients’ Laplace noise scale parameters. At last, we evaluate our FGS-Pufferfish privacy mechanism on both simulated and real-life data and find that our proposed mechanism achieves better data utility and privacy compared with the other state-of-the-art existing approach.

Authors:

^[1];

^[2]; Yin, Hui ^[3]; Jia, Xiaohua ^[4]

Hunan Univ., Changsha (China). College of Computer Science and Eletronic Engineering
Argonne National Lab. (ANL), Argonne, IL (United States); Illinois Inst. of Technology, Chicago, IL (United States). Dept. of Electrical and Computer Engineering
Hunan Univ., Changsha (China). College of Computer Science and Eletronic Engineering; Changsha Univ. (China). College of Computer Engineering and Applied Mathematics
CIty Univ. of Hong Kong (China). Dept. of Computer Science

Publication Date:: 2018-06-18

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:: National Science Foundation of China (NSFC)

OSTI Identifier:: 1462750

Grant/Contract Number:: AC02-06CH11357; 61732022; 61472131; 61472132; 61772191; 2015TP1004; 2015SK2087; 2015JC1001; 2016JC2012; 2017JJ2292; 17B030; K1705018

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Access

Additional Journal Information:: Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2169-3536

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Fourier coefficients; Lagrange multiplier method; Pufferfish privacy; geometric sum; temporally correlated trajectories

Citation Formats


                    Ou, Lu, Qin, Zheng, Liao, Shaolin, Yin, Hui, and Jia, Xiaohua. An Optimal Pufferfish Privacy Mechanism for Temporally Correlated Trajectories.  United States: N. p., 2018. 
Web.  doi:10.1109/ACCESS.2018.2847720.

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                    Ou, Lu, Qin, Zheng, Liao, Shaolin, Yin, Hui, & Jia, Xiaohua. An Optimal Pufferfish Privacy Mechanism for Temporally Correlated Trajectories.  United States.  https://doi.org/10.1109/ACCESS.2018.2847720

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                    Ou, Lu, Qin, Zheng, Liao, Shaolin, Yin, Hui, and Jia, Xiaohua. Mon .  
"An Optimal Pufferfish Privacy Mechanism for Temporally Correlated Trajectories".  United States.  https://doi.org/10.1109/ACCESS.2018.2847720.  https://www.osti.gov/servlets/purl/1462750.

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@article{osti_1462750,

  title        = {An Optimal Pufferfish Privacy Mechanism for Temporally Correlated Trajectories},

  author       = {Ou, Lu and Qin, Zheng and Liao, Shaolin and Yin, Hui and Jia, Xiaohua},

  abstractNote = {Temporally correlated trajectories are ubiquitous, and it has been a challenging problem to protect the temporal correlation from being used against users’ privacy. In this work, we propose an optimal Pufferfish privacy mechanism to achieve better data utility while providing guaranteed privacy of temporally correlated daily trajectories. First, a Laplace noise mechanism is realized through geometric sum of noisy Fourier coefficients of temporally correlated daily trajectories. Then, we prove that the proposed noisy Fourier coefficients’ geometric sum satisfies Pufferfish privacy, i.e. , the so-called FGS-Pufferfish privacy mechanism. Furthermore, we achieve better data utility for a given privacy budget by solving a constrained optimization problem of the noisy Fourier coefficients via the Lagrange multiplier method. What is more, a rigorous mathematical formula has been obtained for the Fourier coefficients’ Laplace noise scale parameters. At last, we evaluate our FGS-Pufferfish privacy mechanism on both simulated and real-life data and find that our proposed mechanism achieves better data utility and privacy compared with the other state-of-the-art existing approach.},

  doi          = {10.1109/ACCESS.2018.2847720},

  journal      = {IEEE Access},

  number       = 1,

  volume       = 6,

  place        = {United States},

  year         = {2018},

  month        = {6}

}

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