Location Entropy-Based Privacy Protection Algorithm for Social Internet of Vehicles

In the Social Internet of Vehicles (SIoV), sharing data among entities is prone to leaking private data. Protecting vehicle users privacy through encryption and anonymous method will generate large service overhead. To protect vehicle users location privacy with low service overhead, a location entropy-based privacy protection algorithm for Social Internet of Vehicles (LEPPV) has been proposed. Location entropy is used to measure the uncertainty of vehicle users’ destination. The higher the location entropy, the higher the level of vehicle user location privacy protection. We use two methods to increase location entropy: vehicle users request the type of service rather than the content of the service; Multiple Point of Interests (POIs) are screened out for vehicle users. The roadside units (RSUs) actively caches surrounding POIs, so that the service overhead generated by service requests can be reduced. We verify the effectiveness of the proposed algorithm in Veins. The experimental results show that our algorithm is able to protect vehicle user location privacy while ensuring service quality.