Journal
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
Volume 24, Issue 1, Pages 25-49Publisher
SPRINGER
DOI: 10.1007/s11280-020-00830-x
Keywords
Location-based service; Location privacy; Query privacy; Privacy protection
Funding
- Zhejiang Provincial Natural Science Foundation of China [LZ18F020001, LY19F020018]
- National Natural Science Foundation of China [61762055, 61702468, 61962029]
- National Social Science Foundation of China [19BTQ056]
- Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing [KLIGIP-2018B03]
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This paper proposes a method to protect user privacy in LBS by constructing dummy query sequences to conceal user query locations and attributes, effectively protecting location privacy and attribute privacy.
Location-based services (LBS) have become an important part of people's daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users' privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.
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