Social Internet of Vehicles (SIoV) is an emerging complex network where the features of Social Networks are applied to the SIoV system. User location data forms the basis for the implementation of SIoV functions. However, this type of data contains a large amount of user personal information, which may cause privacy leakage if it is stolen. Protecting the privacy of the user location can eliminate concerns about the leakage of user personal privacy data, increase users' viscosity, and help to contribute to the improvement of the SIoV system. This paper systematically analyzes the location privacy protection technology utilized in recent years in the field of SIoV, proposes three types of user data location privacy protection technology, and evaluates the performance of these technologies. We further present some potential future research directions for location privacy protection technology through the analysis and summary of existing work.

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Spatial crowdsourcing assigns location-related tasks to a group of workers (people equipped with smart devices and willing to complete the tasks), who complete the tasks according to their scope of work. Since space crowdsourcing usually requires workers’ location information to be uploaded to the crowdsourcing server, it inevitably causes the privacy disclosure of workers. At the same time, it is difficult to allocate tasks effectively in space crowdsourcing. Therefore, in order to improve the task allocation efficiency of spatial crowdsourcing in the case of large task quantity and improve the degree of privacy protection for workers, a new algorithm is proposed in this paper, which can improve the efficiency of task allocation by disturbing the location of workers and task requesters through k-anonymity. Experiments show that the algorithm can improve the efficiency of task allocation effectively, reduce the task waiting time, improve the privacy of workers and task location, and improve the efficiency of space crowdsourcing service when facing a large quantity of tasks.

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Due to the high mobility of vehicles and the high dynamics of SIoV network topology, the communication between users will be frequently interrupted, thus affecting the service quality of users. In addition, due to the open nature of the SIoV wireless channel, any user can broadcast messages in the system. However, unreliable users pose serious security threats to other users on the network. In order to solve these problems, we propose a feature cluster-based secure data transmission method (FC-SDTM) to ensure safe and stable data transmission between vehicles. This method creates feature clusters according to the feature similarity of users, which provides the stability of communication between users and improves users’ reliability in the cluster. Second, consortium blockchains store the transmission data sent by the sender in the cluster for the receiver to verify, further ensuring the security of intra-cluster communication. Finally, the random number key reduces the running time of the proposed method and solves the security problem caused by cluster topology updates. The experimental results demonstrate that this method can reduce the system running time and the message exposure rate, while also improving transmission accuracy.