As an emerging complex social network, the Social Internet of Vehicles (SIoV) potentially exposes user location privacy. In this letter, we propose a location privacy protection method based on double ${k}$ -anonymity that hides user locations and request information. The cloud server is introduced as a trusted third party to isolate the direct communication between users and the service provider, while correlation between identities and requests is also reduced by means of a permutation and combination method. Extensive simulations are conducted to demonstrate that the method can protect user location privacy to the greatest extent possible while still ensuring service availability.

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.