大数据

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基于指数机制的轨迹差分隐私保护方法

焦荟聪,刘文菊,王赜   

  1. 天津工业大学 计算机科学与技术学院,天津 300384

Differential Privacy Protection method Based on Exponential Mechanism

JIAO Hui-cong,LIU Wen-ju,WANG Ze   

  1. College of Computer Science and Technology, Tiangong University, Tianjin 300384, China

摘要: 针对传统轨迹数据保护中忽略位置点携带的语义信息带来的隐私泄露问题,本文提出一种基于指数机制的差分隐私轨迹保护方法。本文根据差分隐私中指数机制的特性,同时考虑地理特征以及位置语义特征双重属性信息导致的隐私泄露,对位置点设计可用的打分函数后随机化输出,对轨迹进行了有效的隐私保护。该方案使得在保证位置隐私的同时减小数据集规模,同时可以防止语义背景推断攻击,提高数据可用性。本文在真实轨迹数据集上进行了实验,实验结果表明该方法在保证隐私强度下,既有效保护了用户的停留区域位置隐私,同时有效的提高了数据可用性。

关键词: 差分隐私, 时空轨迹, 语义位置, 指数机制

Abstract: Aiming at the problem of privacy disclosure caused by ignoring semantic information carried by location points in traditional trajectory data protection, a differential privacy trajectory protection method based on exponential mechanism is proposed in this paper. According to the characteristics of the index mechanism in differential privacy, and considering the privacy disclosure caused by the dual attribute information of geographic features and semantic features of location, this paper designs an available scoring function for location points and then randomizes the output to effectively protect the trajectory privacy. This scheme can reduce the size of data sets while ensuring location privacy, prevent semantic background inference attacks and improve data availability. In this paper, experiments are carried out on real trajectory data sets, and the experimental results show that the proposed method not only effectively protects the privacy of the user's stay area location, but also effectively improves the data availability while ensuring the privacy intensity.

Key words: Differential privacy, Space-time trajectory, Semantic location, Index mechanis

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