Location Correlated Differential Privacy Protection Based on Mobile Feature Analysis

Recently, with the popularity of smartphones and other GPS embedded devices, location-based service applications are being rapidly developed. In addition, individual privacy protection is also receiving increasing attention. Currently, most studies assume that individual location records are independent. However, the records are mostly interrelated in the real world. If the information is protected without considering the location-correlated information between users, an attacker can use a background knowledge attack to obtain the user's private information. Therefore, this paper proposes a method to protect multiuser location-correlated information under a strict privacy budget. First, a method for group movement analysis based on adaptive time segmentation is proposed in this paper. In addition, based on the time dimension, time-continuous hotspot areas are constructed by adaptively segmenting and merging the stay areas, which are established for subsequent location-correlated privacy protection. Second, a data publishing mechanism is proposed to resist inferred attacks and to adaptively protect user-correlated location information. In addition, this paper also proposes the individual user correlation sensitivity concept and extends differential privacy by building an individual sensitivity matrix to correct noise. The experiments on real datasets show that under the same conditions, compared with the existing methods, the heat value of the hotspot areas formed by the method is increased by 10.11% under the same time slice length. In addition, the method reduces the similarity of 26.98% of group users.