Location privacy-preserving k nearest neighbor query under user’s preference
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- 作者：Weiwei Ni ; wni@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Mingzhu Gu gumingzhu@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Xiao Chen chenxiao@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author
- 关键词：Location based service ; Location privacy model ; Privacy preference ; k nearest neighbor query ; Farthest POI attack
- 刊名：Knowledge-Based Systems
- 出版年：2016
- 出版时间：1 July 2016
- 年：2016
- 卷：103
- 期：Complete
- 页码：19-27
- 全文大小：1392 K
文摘
Location-based services can provide users’ surroundings anywhere and anytime. While this service brings convenience for users, the disclosure of user’s location becomes the main concerns. Most current practices fall into K-anonymity model, in parallel with location cloaking. This schema commonly suffers from the following constraints. (1) K-anonymity cannot support users’ preferential query requirements effectively. (2) location cloaking commonly assumes that there exists a trusted third party to serve as anonymizer, which is inclined to be the bottleneck of the query. Concerning these problems, a novel location privacy model (s, ε)-anonymity is devised from perspective of minimum inferred region and candidate answer region, which present location protection strength and scale of intermediate results, respectively. Particularly, user’s preferential query requirements on privacy protection strength and query efficiency can be presented in a more convenient and effective way by setting parameters s and ε rather than K-anonymity model does. A thin server solution is developed to realize the model, which pushes most workload originated from user’s preferential requirement down to client side leveraging false query technology without any trusted third parties’ intervention. Furthermore, an entropy based strategy is devised to construct candidate answer region, which boosts privacy protection strength and query efficiency simultaneously. Theoretical analysis and empirical studies demonstrate our implementation delivers well trade-off among location protection, query performance and query user’s privacy preference.