基于位置的服务中基于用户的语义位置隐私保护方法

Xudong Yang, Ling Gao, Hai Wang, Yan Li, Jie Zheng, Jipeng Xu, Yuhui Ma
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引用次数: 2

摘要

随着基于位置的服务(LBS)的普及和发展,位置隐私保护成为近年来的研究热点,尤其是k-匿名的研究。虽然以往的研究在隐私保护方面做了大量的工作,但忽视了攻击者所拥有的与用户相关的位置语义信息知识对安全性的负面影响。为了解决这个问题,我们提出了一种基于k-匿名的用户相关语义位置隐私保护机制(USPPM)。首先,提出将用户相关移动位置语义特征与语义多样性熵相结合的匿名集生成方法,提高位置语义隐私安全性;其次,通过攻击者和保护者之间的stackberg博弈模型,设计了一种增强敏感语义位置隐私的匿名集优化方法。最后,在真实数据集上的实验表明,我们的算法能够有效地提供位置隐私。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A User-related Semantic Location Privacy Protection Method In Location-based Service
With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.
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