满足本地差分隐私的位置数据采集方案

高志强; 崔翛龙; 杜波; 周沙; 袁琛; 李爱

doi:10.16511/j.cnki.qhdxxb.2018.22.058

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清华大学学报（自然科学版） ›› 2019, Vol. 59 ›› Issue (1) : 23-27. DOI: 10.16511/j.cnki.qhdxxb.2018.22.058

信息安全

满足本地差分隐私的位置数据采集方案

高志强, 崔翛龙, 杜波, 周沙, 袁琛, 李爱

作者信息 +

Collection scheme of location data based on local differential privacy

GAO Zhiqiang, CUI Xiaolong, DU Bo, ZHOU Sha, YUAN Chen, LI Ai

Author information +

文章历史 +

摘要

针对位置数据采集中的隐私保护问题，该文给出了基于本地差分隐私的位置数据采集方案。采用多阶段随机应答机制进行满足本地差分隐私的位置数据采集；以区域密度估计为目标，分别利用直接统计法和期望最大法进行位置数据分析。该方案保证不可信数据采集者利用非原始位置数据仍可以实现以统计特征为基础的位置数据分析。大量仿真实验结果表明：该方案在小样本位置数据场景下，期望最大法的可用性和隐私保护特性较优；在大样本位置数据量场景下，直接统计法和期望最大法的性能相近。

Abstract

Methods are needed to protect a person's privacy while monitoring their location. This paper presents a scheme for collecting location data based on local differential privacy. First, a multi-phase randomized response is used to collect the location data based on their local differential privacy. Then, the density of a certain section is estimated using the statistical method and expectation maximization (EM) to analyze the location data. The scheme guarantees that an untrustworthy data collector can still obtain the location statistics without direct access to the original data. Extensive tests verify that EM provides better privacy protection and better utility than the statistical method with limited location data. The results of the statistical method and EM are similar with abundant location data.

导出引用

高志强, 崔翛龙, 杜波, 周沙, 袁琛, 李爱. 满足本地差分隐私的位置数据采集方案[J]. 清华大学学报（自然科学版）. 2019, 59(1): 23-27 https://doi.org/10.16511/j.cnki.qhdxxb.2018.22.058

GAO Zhiqiang, CUI Xiaolong, DU Bo, ZHOU Sha, YUAN Chen, LI Ai. Collection scheme of location data based on local differential privacy[J]. Journal of Tsinghua University(Science and Technology). 2019, 59(1): 23-27 https://doi.org/10.16511/j.cnki.qhdxxb.2018.22.058

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