Affiliations: [a] School of Geography and Tourism, Anhui Normal University, Wuhu, Anhui 241002, China | [b] School of Computer and Information, Anhui Normal University, Wuhu, Anhui 241002, China | [c] Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu, Anhui 241002, China
Correspondence:
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Corresponding author: Yonglong Luo, School of Geography and Tourism, Anhui Normal University, Wuhu, Anhui 241002, China. Tel.: +86 553 5910645; E-mail: [email protected].
Abstract: Existing location-privacy-preserving methods primarily focus on solving the problem of location-privacy preservation in the global space. This not only increases the response time of the location service, it also degrades the data quality. In this paper, a k-anonymity algorithm based on locality-sensitive hashing is proposed to solve the problem of location-privacy preservation in the subspace. In the proposed algorithm, higher efficiency and higher quality of service are achieved by applying a bottom-up grid-search method. Further, reasonable division is obtained based on locality-sensitive hashing by retaining position characteristics. The results of experiments conducted to evaluate the proposed algorithm indicate that the proposed algorithm provides a smaller anonymous spatial region, higher data quality, and lower time cost than methods with no subspace.
