Subspace k-anonymity algorithm for location-privacy preservation based on locality-sensitive hashing
Xiaohan Wang, Yonglong Luo, Shiyang Liu, Taochun Wang, Huihui Han · Intelligent Data Analysis · 2019
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-se nsitive 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.