A Dummy Location Selection Algorithm Based on Location Semantics and Physical Distance

Dongdong Yang, Baopeng Ye, Yuling Chen, Huiyu Zhou, Xiaobin Qian

Abstract: With the development of smart devices and mobile positioning technologies, location-based services (LBS) has become more and more popular. While enjoying the convenience and entertainments provided by LBS, users are vulnerable to the increased privacy leakages of locations as another kind of quasidentifiers. Most existing location privacy preservation algorithms are based on region cloaking which blurs the exact position into a region, and hence prone to inaccuracies of query results. Dummy-based approaches for location privacy preservation proposed recently overcome the above problem, but did not consider the problem of location semantic homogeneity, query probability and physical dispersion of locations simultaneously. In this paper, we propose a dummy location selection algorithm based on location semantics and physical distance (SPDDS) that takes into account both side information, semantic diversity and physical dispersion of locations. SPDDS solves a simplified problem of single objective optimization by uniting the three objectives (location semantic diversity, query probability and physical dispersion of locations) together. The efficiency and effectiveness of the proposed algorithms have been validated by a set of carefully designed experiments. The experimental results also show that our algorithms significantly improve the privacy level, compared to other dummy-based solutions.