In recent years, many methods have been brought forth to ensure the privacy protection of mobile users. These methods can be divided into two categories: spatial regions of occult [
7‐
9] and false position [
10‐
12]. Generally, space technology is used in the hidden location anonymity model k-. The model k-, first proposed in the literature [
13‐
15], refers to the anonymous position occurring when the location information of at least one other individual and the position information of k-1 cannot be distinguished. As a result, the person’s position to meet the position of k- becomes anonymous. Li et al. [
16] also proposed an Interval Cloak algorithm based on binary tree 4. Given that the literature value of
K is set in the system and does not meet the personalized needs of location privacy, Li and Jung [
17] proposed that the k- anonymous user model could be customized to allow the user to specify the degree of anonymity, thereby proposing the Clique Cloak algorithm. Due to the anonymous success rate is low, Liu [
18,
19] proposes an improved algorithm based on the directed graph [
20]. Using Complete Pyramid Data Structure and Incomplete Pyramid Data Structure to maintain the location information of mobile users, and based on these two data structures, basic and adaptive algorithms were proposed. Ma et al. [
21] have proposed a dynamic bottom-up and top-down grid hiding algorithm. Namiot and Sneps-Sneppe [
22] propose Nearest Neighbor Cloak algorithm and Hilbert Cloak algorithm. The system architecture in the above literature consists of a central server structure [
23,
24], which is used in distributed point-to-point structure. In Pan et al. [
25], mobile users, before sending the query to the location server, send grouped requests first through to other peer nodes to form a space area, which will later be sent to the server along with the query. Because the anonymous method will fail in many cases, Namiot [
22] proposes a Hilbert space filling curve high k-anonymous space to build mechanisms to enhance anonymous success rates of the system. Puttaswamy et al. and Rahimi et al. [
26,
27] use a false location technology. In Resch [
28], mobile users generate a false position and location of their true position and send it to the server. Because the attacker cannot identify the true position of the user, the user’s location privacy is effectively protected. In Puttaswamy et al. [
26], mobile users only send specified false position. The server receives incremental nearest neighbor queries based on this false position, and query results are returned to the users; according to the returned results, the users no longer retrieve the answers they want.
The above assumes that all users are moving in a free space. However, in reality, people often walk in the mobile network. Shen and Zhao [
29] first noticed this problem and brought forward the location privacy protection model XStar. However, Shen and Zhao [
29] only consider the simple mobile network (all roads are double line) of the location privacy protection environment. This is considered to be simultaneously a both simple and complex mobile network (including the single line) on the basis of a new location privacy protection method based on a hidden subgraph.