# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
###### Abstract  
During the procedure, a location-based service (LBS) query, the real location provided by the vehicle user may results in the disclosure of vehicle location privacy. Moreover, the point of interest retrieval service requires high accuracy of location information. However, some privacy preservation methods based on anonymity or obfuscation will affect the service quality. Hence, we study the location privacy-preserving method based on dummy locations in this paper. We propose a vehicle location privacy-preservation method based on dummy locations under road restriction in Internet of vehicles (IoV). In order to improve the validity of selected dummy locations under road restriction, entropy is used to represent the degree of anonymity, and the effective distance is introduced to represent the characteristics of location distribution. We present a dummy location selection algorithm to maximize the anonymous entropy and the effective distance of candidate location set consisting of vehicle user's location and dummy locations, which ensures the uncertainty and dispersion of selected dummy locations. The proposed location privacy-preservation method does not need a trustable third-party server, and it protects the location privacy of vehicles as well as guaranteeing the LBS quality. The performance analysis and simulation results show that the proposed location privacy-preservation method can improve the validity of dummy locations and enhance the preservation of location privacy compared with other methods based on dummy locations. privacy preservation; Internet of vehicles (IoV); location-based services (LBS); location privacy; dummy location; effective distance +
Footnote †: journal: Applied Sciences  
To address the privacy-preserving issue, many approaches have been proposed over the past few years. Most of them are based on the location perturbation and obfuscation adopt well-known privacy metrics such as K-anonymity [6] and rely on a trusted third-party server [7; 8]. However, K-anonymity privacy-preserving scheme is suitable for high vehicle density. When there are fewer vehicles, spatial anonymity may not be realized, or the anonymous area formed is too large [9]. On the other hand, for the point of interest (POI) retrieval service in IoV, the accuracy of retrieval results is related to the precision of provided location information. However, the location privacy-preservation schemes based on anonymity or obfuscation cannot guarantee the accuracy of location information, which affects the quality of LBS [10; 11; 12; 13]. In the location privacy-preservation method based on dummy locations, a location set containing (or implied) the user's real location is provide to the LBS server. Hence, this method can ensure the accuracy of POI retrieval results [14]. At the same time, the generation of dummy locations does not need a trustable third-party server. In recent years, many location privacy-preservation methods based on dummy locations have been proposed [15; 16; 17; 18; 19; 20; 21; 22; 23]. However, due to the characteristics of vehicles, the location of vehicles is subject to the road distribution, many methods cannot be directly adopted in IoV. In IoV, road information can be used to preprocess dummy locations by the LBS server. Since the enhanced dummy location selection (E-DLS) [18] algorithm does not take the road information into consideration, the validity of dummy locations cannot be guaranteed. In addition, due to the restriction of roads and roadside buildings, the distribution of dummy locations is constrained. Although dummy locations are generated combining with location semantic information [24], the required location distribution is difficult to be achieved under road constraints. Considering the geographical constraint, a method is proposed to generate and arrange dummy objects around users in a grid form [25]. However, the method ignores the history request information.