Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (11): 3252-3257.DOI: 10.11772/j.issn.1001-9081.2018040861

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Trajectory privacy-preserving method based on information entropy suppression

WANG Yifei1,2, LUO Yonglong1,2, YU Qingying1,2, LIU Qingqing1,2, CHEN Wen1,2   

  1. 1. School of Computer and Information, Anhui Normal University, Wuhu Anhui 241003, China;
    2. Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu Anhui 241003, China
  • Received:2018-04-25 Revised:2018-07-12 Online:2018-11-10 Published:2018-11-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61672039, 61702010), the Natural Science Foundation of Anhui Province (1808085MF172), the University Natural Science Research Program of Anhui Province (KJ2017A327), the Science and Technology Project of Wuhu City (2016cxy04).


汪逸飞1,2, 罗永龙1,2, 俞庆英1,2, 刘晴晴1,2, 陈文1,2   

  1. 1. 安徽师范大学 计算机与信息学院, 安徽 芜湖 241003;
    2. 安徽师范大学 网络与信息安全安徽省重点实验室, 安徽 芜湖 241003
  • 通讯作者: 汪逸飞
  • 作者简介:汪逸飞(1993-),男,安徽潜山人,硕士研究生,主要研究方向:信息安全、隐私保护;罗永龙(1972-),男,安徽太湖人,教授,博士生导师,博士,CCF会员,主要研究方向:信息安全、空间数据处理;俞庆英(1980-),女,安徽黄山人,副教授,博士研究生,主要研究方向:空间数据处理、信息安全;刘晴晴(1994-),安徽宿州人,硕士研究生,主要研究方向:信息安全、隐私保护;陈文(1979-),男,安徽铜陵人,教授,博士研究生,主要研究方向:空间数据处理、信息安全。
  • 基金资助:

Abstract: Aiming at the problem of poor data anonymity and large data loss caused by excessive suppression of traditional high-dimensional trajectory privacy protection model, a new trajectory-privacy method based on information entropy suppression was proposed. A flowgraph based on entropy was generated for the trajectory dataset, a reasonable cost function according to the information entropy of spatio-temproal points was designed, and the privacy was preserved by local suppression of spatio-temproal points. Meanwhile, an improved algorithm for comparing the similarity of flowgraphs before and after suppression was proposed, and a function for evaluating the privacy gains was introduced.Finally, the proposed method was compared with the LK-Local (Length K-anonymity based on Local suppression) approach in trajectory privacy and data practicability. The experimental results on a synthetic subway transportation system dataset show that, with the same anonymous parameter value the proposed method increases the similarity measure by about 27%, reduces the data loss by about 25%, and increases the privacy gain by about 21%.

Key words: privacy-preserving, trajectory suppression, spatio-temproal point, flowgraph, information entropy

摘要: 针对传统高维轨迹隐私保护模型抑制点数过多而导致的数据匿名性差及数据损失大的问题,提出了一种基于信息熵抑制的轨迹隐私保护方法。通过为轨迹数据建立基于熵的流量图,根据轨迹时空点信息熵大小设计合理的花费代价函数,局部抑制时空点以达到隐私保护的目的;同时改进了一种比较抑制前后流量图相似性的算法,并提出了一个衡量隐私收益的函数;最后,与LK-Local方法进行了轨迹隐私度与数据实用性的比较。在模拟地铁交通运输系统数据集上的实验结果表明,与LK-Local方法相比,在相同的匿名参数取值下,所提方法在相似性度量上提高了约27%,在数据损失度量上降低了约25%,在隐私收益上提高了约21%。

关键词: 隐私保护, 轨迹抑制, 时空点, 流量图, 信息熵

CLC Number: