Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (2): 104-112.DOI: 10.3778/j.issn.1002-8331.2008-0087

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Trajectory-Clustering Based Privacy Protection Method for Continuous Query in LBS

LYU Xin, ZHAO Liancheng, YU Jiyuan, TAN Bin, ZENG Tao, CHEN Juan   

  1. 1.College of Computer and Information, Hohai University, Nanjing 210098, China
    2.Huaneng Lancang River Hydropower Co., Ltd., Kunming 650214, China
  • Online:2021-01-15 Published:2021-01-14

基于轨迹聚类的连续查询隐私保护方法

吕鑫,赵连成,余记远,谭彬,曾涛,陈娟   

  1. 1.河海大学 计算机与信息学院,南京 210098
    2.华能澜沧江水电股份有限公司,昆明 650214

Abstract:

In reality, users usually submit queries continuously to LBS providers, inevitably, some background information, such as time sequence of query, location area, moving trend, is collected and analyzed by the attackers to obtain the real location or trajectory, which will lead to the exposure of user’s habits and other privacy information. In this paper, a trajectory-clustering based privacy protection method is proposed for continuous query application. The core of the method is to design an effective mechanism for constructing anonymous region, on the basis of the collaboration of the neighboring users. During the query process, users firstly retrieve the target results from the shared cache, if it missed, then it launches a query request to the LBS server. Meanwhile, a location updating algorithm for the adjacent users is proposed to improve the collaboration efficiency and guarantee the availability of cache information. Besides, for cache hitting, a region of interest extraction algorithm based on density clustering is utilized to generate fake query with high confusion to disturb the order of the whole query sequence, thereby enhancing the performance of trajectory privacy protection. The experimental results show that the time cost of continuous query is reduced, and the confusion degree is improved at the same time.

Key words: Location Based Service(LBS) continuous query, privacy protection, collaboration, trajectory clustering, fake query

摘要:

在LBS连续查询的应用场景下,攻击者易利用查询时间序列、区域位置、移动趋势等背景知识发起有效的攻击,以获取用户的真实位置或轨迹,进而可推断出用户生活习惯等各类隐私信息。针对此,提出了一种基于轨迹聚类的连续查询隐私保护方法。该方法基于邻近用户的信息共享与协作,设计了一种匿名区域构造机制,用户在查询过程中,首先通过被共享缓存获取所需服务结果,如未命中,再向LBS服务器发起查询请求。同时,提出了一种邻近用户位置更新算法,提高用户的协作效率并保证缓存的有效性,对于由命中缓存完成的查询,采用提出的基于密度聚类的兴趣区提取算法,生成高混淆度的假查询扰乱整体查询序列顺序,以此增强轨迹隐私的保护效果。实验结果表明,该方法降低了连续查询中的时间代价,提高了位置混淆程度。

关键词: 基于位置的服务(LBS)连续查询, 隐私保护, 用户协作, 轨迹聚类, 假查询