计算机工程与应用 ›› 2020, Vol. 56 ›› Issue (14): 74-81.DOI: 10.3778/j.issn.1002-8331.1904-0379

• 网络、通信与安全 • 上一篇    下一篇

面向连续查询的敏感语义位置隐私保护方案

王永录,左开中,曾海燕,刘蕊,郭良敏   

  1. 1.安徽师范大学 计算机与信息学院,安徽 芜湖 241002
    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 241002
  • 出版日期:2020-07-15 发布日期:2020-07-14

Sensitive-Semantic Location Privacy Protection for Continuous Query

WANG Yonglu, ZUO Kaizhong, ZENG Haiyan, LIU Rui, GUO Liangmin   

  1. 1.School of Computer and Information,Anhui Normal University, Wuhu, Anhui 241002, China
    2.Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu, Anhui 241002, China
  • Online:2020-07-15 Published:2020-07-14

摘要:

针对连续查询位置服务中构造匿名区域未考虑语义位置信息导致敏感隐私泄露问题,通过设计[(K,θ)]-隐私模型,提出一种路网环境下面向连续查询的敏感语义位置隐私保护方案。该方案利用Voronoi图将城市路网预先划分为独立的Voronoi单元,依据用户的移动路径和移动速度,选择具有相似特性的其他[K-1]个用户,构建匿名用户集;利用匿名用户集用户设定的敏感语义位置类型和语义安全阈值,以及用户所处语义位置的Voronoi单元,构建满足[(K,θ)]-隐私模型的语义安全匿名区域,可以同时防止连续查询追踪攻击和语义推断攻击。实验结果表明,与SCPA算法相比,该方案在隐私保护程度上提升约15%,系统开销上降低约20%。

关键词: 基于位置服务, 连续查询, 语义位置, 隐私保护, [(K, &theta, )]-隐私模型

Abstract:

Aiming at the problem of constructing anonymous area in continuous query location service without considering semantic location information and causing sensitive privacy leakage, a sensitive-semantic location privacy protection scheme for continuous query under the road network environment is proposed by [(K,θ)]-privacy model. The urban road network is pre-divided into independent Voronoi cells by Voronoi diagram. According to the user’s moving path and moving speed, other [K-1] users with similar characteristics are selected to construct an anonymous user set. It utilizes the sensitive semantic location type and semantic security threshold by the user of anonymous user set, as well as the Voronoi cell of the user’s semantic location, constructs semantic security anonymous regions that satisfie the [(K,θ)]-privacy model, and simultaneously prevents continuous query tracking attacks and semantic inference attacks. The experimental results show that compared with the SCPA algorithm, the scheme has increased the privacy protection by about 15% and reduced the system overhead by about 20%.

Key words: location-based service, continuous query, semantic location, privacy protection, [(K,θ)]-privacy model