Privacy-Preserving Skyline Queries in LBS

Abstract

Skyline query is widely used in many applications, such as multi-criteria decision making, data mining and visualization, as well as Location-Based Services (LBS). The previous works about skyline mainly focuses on static attributes, such as Branch and Bound Skyline and Probabilistic Skyline. However, due to the requirements in the privacy-protection as protecting individual position and individual information in LBS, a cloaking region of a user instead of his exact position should be cared. To protect privacy of users’ location, dynamic attribute such as uncertain user position should be introduced to skyline. In this paper, two novel skylines query, Range to Ranges Skyline Query (R2R Skyline Query) and Point to Ranges Skyline Query (P2R Skyline Query), are introduced to deal with the privacy problems in LBS. Firstly we propose a R2RSQ algorithm, based on effectiveness pruning mechanism, to answer R2R skyline query, where the spatial attributes of data are all dynamic. Then, R2RSQ algorithm is extended to solve P2R skyline query by its generality. Lastly, extensive experiments using real data sets demonstrate the efficiency and effectiveness of our proposed algorithms in answering R2R skyline query. Our experimental results show that Privacy-Preserving skylines are significant and useful, and R2RSQ algorithm can efficiently support Privacy-Preserving skylines, especially, R2RSQ is efficient on large datasets with dynamic attributes.