Collaborative trajectory privacy preserving scheme in location-based services

详细信息    查看全文

• 作者：Tao Peng^a ; Qin Liu^b ; Dacheng Meng^a ; Guojun Wang ; ^c ; ^a ; ^{csgjwang@gmail.com}
• 关键词：K-anonymity ; Location-based service (LBS) ; Location privacy ; Trajectory privacy preserving ; Continuous query
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：May 2017
• 年：2017
• 卷：387
• 期：Complete
• 页码：165-179
• 全文大小：1462 K
• 卷排序：387

文摘

Location-based services (LBSs) have been gaining considerable popularity and are becoming the fastest growing activity-related services that people use in their daily life. While users benefit from LBSs, the collection and analysis of participators’ location data and trajectory information may jeopardize their privacy. Existing proposals focus mostly on snapshot queries. However, privacy preservation in continuous LBSs is more challenging than in snapshot queries because adversaries could use the spatial and temporal correlations on the user trajectory to infer the user’s private information. In this paper, we propose the collaborative trajectory privacy preserving (CTPP) scheme for continuous queries, in which trajectory privacy is guaranteed by caching-aware collaboration between users, without the need for any fully trusted entities. The main idea of our scheme is to obfuscate the actual trajectory of a user by issuing fake queries to confuse the LBS adversary. We first present a multi-hop caching-aware cloaking algorithm to collect valuable information from multi-hop peers based on collaborative caching. Then, we describe a collaborative privacy preserving querying algorithm that issues a fake query to confuse the location service provider (LSP). Extensive experimental results verify the effectiveness and efficiency of our scheme in terms of processing time and communication cost.