%0 Journal Article
%T A Trusted and Privacy-Preserving Carpooling Matching Scheme in Vehicular Networks
%A Hongliang Sun
%A Linfeng Wei
%A Libo Wang
%A Juli Yin
%A Wenxuan Ma
%J Journal of Information Security
%P 1-22
%@ 2153-1242
%D 2022
%I Scientific Research Publishing
%R 10.4236/jis.2022.131001
%X 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.
%K Vehicular Networks
%K Carpooling Matching
%K Travel Preference
%K Bloom Filter
%K Privacy Set Intersection
%K Trust Management
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=114903