摘要
With the rapid development of intelligent transportation, carpooling with the help of Vehicular Networks plays an important role in improving transportati<span>on efficiency and solving environmental problems. However, attackers us</span>ually launch attacks and cause privacy leakage of carpooling users. In addition, the trust issue between unfamiliar vehicles and passengers reduces the efficiency of carpooling. To address these issues, this paper introduced a trusted and pr<span>ivacy-preserving carpooling matching scheme in Vehicular Networks (T</span>PCM). TPC<span>M scheme introduced travel preferences during carpooling matching, according to the passengers’ individual travel preferences needs, which adopt</span>ed th<span>e privacy set intersection technology based on the Bloom filter to match t</span>he passengers with the vehicles to achieve the purpose of protecting privacy an<span>d meeting the individual needs of passengers simultaneously. TPCM sch</span>eme adopted a multi-faceted trust management model, which calculated the trust val<span>ue of different travel preferences of vehicle based on passengers’ carp</span>ooling feedback to evaluate the vehicle’s trustworthiness from multi-faceted when carpooling matching. Moreover, a series of experiments were conducted to verify the effectiveness and robustness of the proposed scheme. The results show that the proposed scheme has high accuracy, lower computational and communication costs when compared with the existing carpooling schemes.
With the rapid development of intelligent transportation, carpooling with the help of Vehicular Networks plays an important role in improving transportati<span>on efficiency and solving environmental problems. However, attackers us</span>ually launch attacks and cause privacy leakage of carpooling users. In addition, the trust issue between unfamiliar vehicles and passengers reduces the efficiency of carpooling. To address these issues, this paper introduced a trusted and pr<span>ivacy-preserving carpooling matching scheme in Vehicular Networks (T</span>PCM). TPC<span>M scheme introduced travel preferences during carpooling matching, according to the passengers’ individual travel preferences needs, which adopt</span>ed th<span>e privacy set intersection technology based on the Bloom filter to match t</span>he passengers with the vehicles to achieve the purpose of protecting privacy an<span>d meeting the individual needs of passengers simultaneously. TPCM sch</span>eme adopted a multi-faceted trust management model, which calculated the trust val<span>ue of different travel preferences of vehicle based on passengers’ carp</span>ooling feedback to evaluate the vehicle’s trustworthiness from multi-faceted when carpooling matching. Moreover, a series of experiments were conducted to verify the effectiveness and robustness of the proposed scheme. The results show that the proposed scheme has high accuracy, lower computational and communication costs when compared with the existing carpooling schemes.
