Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users
Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users
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DOI：
10.1155/2020/8892321
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发表时间：
2020-09-01
影响因子：
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通讯作者：
Cai, Zhipeng
中科院分区：
文献类型：
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作者：
Siddula, Madhuri;Li, Yingshu;Cai, Zhipeng
虽然社交网站因其好友网络而大受欢迎，但由于在系统中纳入了基于位置的服务（LBS），用户隐私问题也随之产生。偏好型LBS结合用户的社交资料及其位置来生成个性化推荐系统。由于用户资料和位置历史记录的可获取性，我们经常会将敏感信息泄露给不希望的对象。因此，为这类偏好型LBS请求提供位置隐私已变得至关重要。然而，当前的技术侧重于通过粒度泛化来匿名化位置。这类系统虽然提供了所需的隐私，但却以失去准确推荐为代价。因此，在本文中，我们提出一种新颖的位置隐私保护机制，该机制通过k - 匿名性提供位置隐私，并提供最准确的结果。针对移动用户和情境感知LBS请求的实验结果证明，所提出的方法优于现有方法。
While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user's social profile along with their location to generate personalized recommender systems. With the availability of the user's profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy throughk-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.