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, timecontinuous 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.INDEX TERMS Correlated differential privacy, location protection, mobile feature analysis, time division.
I. INTRODUCTIONRecently, with the popularity of GPS mobile devices, location-based service applications have been widely used in social and commercial fields. However, users generate a large amount of spatiotemporal data when using these services, which leads to leaking sensitive personal information. Therefore, it is vital to protect users' spatiotemporal data [1].The associate editor coordinating the review of this manuscript and approving it for publication was Kaigui Bian.Taking a wide view of current studies on the privacy protection of user location information, most of the methods assume that the dataset is independent and the internal data records are also independent. However, in real life, people have their social circles, and they often engage in certain social activities [2]. For example, some colleagues in a company may arrive at a designated place within a specified time in the morning and have breakfast together. Therefore, combining location data and time can reflect some of the user's social attributes [3], such as their home address, eating