A trust-based privacy-preserving friend matching scheme in social Internet of Vehicles

Abstract

In this paper, to expand the social circle of vehicle users in the non-trusted network environment, a trust-based privacy-preserving friend matching scheme in social Internet of Vehicles is designed, which can prevent the leakage of sensitive information caused by the matching of user attributes, and make the interaction between vehicles faster and more convenient. The proposed scheme can efficiently evaluate the credibility of the vehicles, and eliminate the malicious vehicles to protect the privacy information of the vehicles. Particularly, we use Bloom Filters to convert the attribute matching problem of the vehicles into the number of calculated intersection elements to judge the matching degree of the interests of both parties. The vehicles that fulfill the matching requirements can add each other as friend according to their interests, avoiding the abuse and disclosure of sensitive information by malicious vehicles. The theoretical analysis demonstrates that the proposed scheme is effective and accurate in privacy protection when vehicle attribute information matching. Simulation results illustrate that the proposed scheme can support large-scale data sets, be effectively applied to network environments with complex information types and rich data content, and satisfy the real-time demand in social Internet of Vehicles.