一种感知时间缓存的虚拟选择定位算法

Xuejiao Mu, Hong Shen, Zhigang Lu
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引用次数: 1

摘要

随着基于位置的服务(LBS)的普及,我们的日常生活越来越便利,例如寻找餐馆和预订出租车,人们对与LBS共享位置时隐私泄露风险的担忧也在增加,并成为阻碍LBS广泛采用的主要瓶颈[1]。在LBS中,为了保护隐私,采用k-匿名来隐藏人们的敏感信息,防止再次识别攻击[2]。不幸的是,k-匿名技术依赖于预定义的对手背景知识。一旦对手有了不同的辅助信息,我们就不能保证对这样的对手有任何隐私保护。为了解决朴素k-匿名的隐私泄露问题,提出了一种结合k-匿名和位置查询频率算法的缓存感知虚拟选择算法(CaDSA)[3]。CaDSA通过在固定时间段(比如一天)内以相似的查询频率对给定区域内的位置进行分组,从而对它们进行匿名化。然而,考虑到在现实生活中,即使在一天中,位置的查询频率也经常在不同的时间段发生变化,如果我们将固定时间段的位置粗略地分组为CaDSA,显然会丢失隐私。因此,在本文中,我们提出了一种时间缓存感知的虚拟位置选择算法(T-CaDLSA),该算法考虑了给定时间段(一天)内不同时隙位置查询频率之间的差异。数学和实验评估表明,在达到相同的数据效用的情况下,我们的方法优于现有的隐私保证工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Temporal Caching-Aware Dummy Selection Location Algorithm
Along with the increased convenience of our daily life thanks to the proliferation of location-based service (LBS), such as finding restaurants and booking taxi, concerns on privacy disclosure risks in sharing our locations with LBS have also increased and become a major bottleneck that obstacles the widespread of adoption of LBS [1]. To preserve privacy in LBS, k-anonymity was applied to conceal people's sensitive information against re-identification attacks [2]. Unfortunately, the k-anonymity technique relies on predefined background knowledge of an adversary. Once the adversary has different auxiliary information, we cannot guarantee any privacy preservation against such an adversary. To address the privacy leakage problem of the naive k-anonymity, a combination of k-anonymity and location's query frequency algorithm, the Caching-aware Dummy Selection Algorithm (CaDSA), were proposed [3]. CaDSA anonymises locations in a given area by grouping them with similar query frequency during a fixed time period, say one day. However, considering in the real-life situation location's query frequency often varies in different time slots even in a single day, privacy will clearly lose if we roughly group locations according to a fixed time period as CaDSA. Consequently, in this paper, we propose a Temporal Caching-aware Dummy Location Selection Algorithm (T-CaDLSA) that considers the differences among location's query frequencies over different time slots within a given time period (day). Both mathematical and experimental evaluations show that to achieve the same data utility, our method outperforms the existing work in privacy guarantee.
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