Privacy Protection Algorithm Based on Optimized Local Suppression for Trajectory Data Publication

doi:10.19678/j.issn.1000-3428.0055431

Abstract

Abstract: To address the problem of privacy leakage caused by trajectory sequences in trajectory data publication,this paper proposes a privacy protection algorithm,TPL-Local,based on optimized local suppression.The algorithm identifies the minimal violating sequence set and determines their suppression modes in the trajectory dataset.Then,the score table of instances in the minimal violating sequence set is constructed,and on this basis the instances are chosen and suppressed according to their scores.To implement privacy protection of trajectory data,global suppression is replaced by local suppression.By reducing the instance loss of global suppression,the data loss rate is reduced and the trajectory data availability is improved.Finally,this paper compares the data utility loss of the proposed algorithm with that of the KCL-Local algorithm on synthetic datasets,and experimental results show that the proposed algorithm can ensure the security of trajectory data while improving the data availability.

Key words: trajectory data, local suppression, global suppression, privacy protection, data availability

摘要： 针对轨迹数据发布中由轨迹序列引起的隐私泄露问题，提出一种基于优化局部抑制的轨迹隐私保护算法TPL-Local。识别轨迹数据集中的最小违反序列集合并判断最小违反序列的抑制方式，对序列中的实例构建得分表，根据分值高低选择相应的实例并进行抑制。采用以局部抑制代替全局抑制的方式实现轨迹数据的隐私保护，通过减少全局抑制损失的实例来降低数据损失率并提高轨迹数据的可用性。在合成数据集上进行数据实用性损失对比实验，结果表明，相比KCL-Local算法，TPL-Local算法能够在保证轨迹数据安全性的同时提高数据的可用性。

关键词: 轨迹数据, 局部抑制, 全局抑制, 隐私保护, 数据可用性

CLC Number:

TP309.2

YU Qingying, WANG Yanfei, YE Zitong, ZHANG Shuanggui, CHEN Chuanming. Privacy Protection Algorithm Based on Optimized Local Suppression for Trajectory Data Publication[J]. Computer Engineering, 2020, 46(8): 112-118.

俞庆英, 王燕飞, 叶梓彤, 张双桂, 陈传明. 基于优化局部抑制的轨迹数据发布隐私保护算法[J]. 计算机工程, 2020, 46(8): 112-118.

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