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 through -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.

中文翻译：

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移动社交网络用户的增强隐私的优先LBS查询

尽管社交网站因其友谊网络而获得了广泛的欢迎，但由于将基于位置的服务（LBS）合并到系统中，因此出现了用户隐私问题。优先LBS会获取用户的社交资料以及他们的位置，以生成个性化的推荐系统。借助用户的个人资料和位置历史记录，我们经常向不希望的当事方透露敏感信息。因此，为这种优先的LBS请求提供位置隐私已变得至关重要。但是，当前的技术集中在通过粒度概括来匿名化位置。这样的系统尽管提供了所需的隐私，但是却以丢失准确的建议为代价。因此，在本文中，我们提出了一种新颖的位置隐私保护机制，该机制可通过以下方式提供位置隐私-匿名并提供最准确的结果。针对移动用户和上下文感知LBS请求的实验结果证明，该方法的性能优于现有方法。