# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 2 Related Work
The protocol achieves two things in parallel, i.e., preserves the location privacy and provides reliable transmission in the network. In [18], the concept of a cryptographic mix zone is used to hide the location information of vehicles that are based on one-time identity-based authentication. It has no dependency on trusted party, and the keys are updated within the zone. Any vehicle in the group may be a group key distributor in the cryptographic zone. A revocable group  
Figure 1: Categories of location privacy schemes. signature scheme is proposed in [37] for location privacy based on the Chinese remainder theorem and digital signature algorithm. It protects the anonymity of the vehicle as well as providing traceability to TA in case of a dispute of signature. However, the group signature method has certain issues regarding the management of signatures in the group. The signature of a large group is difficult to manage, while smaller groups affect the privacy protection level [33]. In [38], a dynamic grouping and virtual pseudonym exchange scheme is proposed in which diverse traffic conditions are utilized to update pseudonyms of vehicles for location protection. Some techniques employ dummy data or inaccurate data to generate confusion for an adversary to identify the real location of a vehicle. In such a case, a perturbation algorithm is proposed in [39] that utilizes the reported position of two users at proximity and slightly modifies their position to create confusion for an adversary. The inaccurate beacon message is sent in between the periodic actual beacon messages to break the link between various locations of vehicles [40]. Dummy locations are generated in [41] for privacy protection of vehicles. In [42], the location of the neighboring vehicle is taken as a virtual shadow and sends two requests to LBS with two different locations. It will hide the actual location of a target vehicle from the location attacker. Similarly, virtual position points are generated in [43] that bridge between user and LBS. The sensitivity-based pseudonym changing scheme is introduced in [44] that takes regularities in vehicle movements to achieve personalised vehicle location protection. A new concept of multilevel obfuscation scheme is introduced in [45] to protect the location privacy of vehicles while communicating with LBS. The vehicle generates duplicate messages in connection with the surrounding vehicle to increase vehicle identity anonymisation in the vicinity. The concept of differential privacy and pseudonym permutation is used in [46] to hide the location trajectory of the vehicle. The trajectory of the user is divided into coarse-grained and fine-grained under the personalized user privacy requirements. Similarly, a new technique is introduced in [47] that protects the user's semantic location trajectory. It uses the concept of reinforcement learning based on differential privacy that randomizes the locations of the vehicle's trajectory. The optimized obfuscation policy is used in terms of privacy improvement and the loss of quality of the services. The obfuscation and hiding in the crowd concept are combined in [48] to increase confusion for an adversary trying to link vehicles' pseudonyms. The dummy-based location privacy scheme improves the level of privacy to some extent. However, there are certain problems related to these schemes, including management of dummy data being an issue, their impact on the quality of services, and generating overhead in the network. \begin{table}
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