# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 2 Related Work  
The location privacy-preserving problem has been attracting wide attention from both academia and industry. This problem draws even more attention due to the booming of LBSs. Many location privacy-preservation methods have been proposed, such as K-anonymity [7; 8; 9; 10], obfuscation [11; 12; 13], differential privacy [26; 27], mixed zone [28; 29], homomorphic encryption [30; 31; 32], and dummy locations [15; 16; 17; 18; 19; 20; 21; 22; 23]. In this work, we focus on the location privacy preservation-method based on dummy locations in IoV. The privacy-preservation method based on dummy locations can work without a third-party server, and provides a location set containing the user's real location to guarantee the quality of LBS. Hence, this method can achieve a good tradeoff between location privacy-preservation and service quality. [PERSON] et al. [15] proposes a privacy-preservation method based on dummy locations, where a query is submitted to an LBS provider with the actual location of a user and other dummy locations. The LBS provider searches for all the related POI locations and returns them to the user. Hence, the generation of dummy locations is the key issue in the privacy-preservation method based on dummy locations. Grid-based and circle-based algorithms for generating dummy locations are proposed to satisfy regional privacy requirements in [16]. A distributed dummy client generation method is proposed to make clients control over their privacy protection [17]. The method selects clients with movement patterns be close to user's movement pattern according to the privacy requirements. However, many dummy location generation algorithms assume that an attacker has no other background information and select locations randomly. To solve this problem, an E-DLS algorithm is proposed in [18]. In the E-DLS algorithm, dummy locations are selected to optimize the privacy-preserving effect in terms of the maximum entropy and cloaking region (CR). Based on E-DLS, [PERSON] et al. [19] considers that when the attacker can obtain the type of service, the greedy algorithm based on entropy measurement is proposed to select the dummy locations to construct the anonymous area. Recently, the trajectory privacy-preserving issue for continuous LBS has been becoming a hot research topic. A method named Dummy-Q is proposed for query privacy-preservation in the continuous LBS scenarios [20], where the query privacy is protected by generating dummy queries. In [21], the problem of privacy leakage under continuous LBS is studied, and a frequency-aware dummy-based method (FADBM) is proposed to ensure that dummy locations are generated around frequent areas and the time accessibility. In [22], a dummy filtering algorithm is proposed, where the spatiotemporal correlation of time-sensitive side information is used to select and generate dummy locations, and spatiotemporal correlation between locations is truncated with time accessibility and access constraints to ensure trajectory similarity. In [23], a location privacy method is proposed to prevent privacy disclosure in LBS constrained in incomplete data collection, where the anonymous candidate set is constructed with compressing sensing technology. Moreover, the differential privacy mechanism is adopted to construct the anonymous candidate set for continuous LBS. Since the trajectory of vehicles is subject to the road distributions, the influence of road information should be considered for designing location privacy-preservation methods in IoV. Considering the geographical constraint, a method is proposed to generate and arrange dummy objects around users in a grid form [25]. However, the service request probability of locations is not considered when generating dummy locations, the effect of privacy protection is poor. [PERSON] et al. [33] proposes a location privacy-preservation scheme based on anonymous entropy, where anonymous entropy based on location distance and request content is considered.