关键词: 基于位置服务, 位置隐私, k-匿名, 边信息, 混淆度

Abstract: With the support of data mining technology, a large number of service data and background knowledge are related to each other, and the generated user query preference, behavior pattern and other side information will drive the attacker to implement more powerful location privacy attack means, which breaks through most existing protection schemes based on k-anonymity. In order to deal with these more powerful privacy threats, this paper proposes a side information fusion dual anonymous location privacy protection scheme SIFDA, that is, it carefully selects other k —1 real users to form k-anonymous set according to diverse query probability and user query preference similarity to resist inference and conspiracy attacks. A novel privacy protection metric is designed to accurately measure the privacy protection effect of anonymous sets. At the same time, the validity of the proposed method is verified by using the real trajectory data set.

Key words: location-based services, location privacy, k -anonymous, side information, confusion degree