期刊
IEEE ACCESS
卷 6, 期 -, 页码 37150-37165出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2847720
关键词
Fourier coefficients; geometric sum; Lagrange multiplier method; Pufferfish privacy; temporally correlated trajectories
资金
- Key Project of National Science Foundation of China [61732022]
- National Science Foundation of China [61472131, 61472132, 61772191]
- Science and Technology Key Projects of Hunan Province [2015TP1004, 2015SK2087, 2015JC1001, 2016JC2012]
- Natural Science Foundation of Hunan Province [2017JJ2292]
- Outstanding Youth Research Project of the Provincial Education Department of Hunan [17B030]
- Science and Technology Planning Project of Changsha [K1705018]
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 paper, 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据