TITLE:
A Trusted and Privacy-Preserving Carpooling Matching Scheme in Vehicular Networks
AUTHORS:
Hongliang Sun, Linfeng Wei, Libo Wang, Juli Yin, Wenxuan Ma
KEYWORDS:
Vehicular Networks, Carpooling Matching, Travel Preference, Bloom Filter, Privacy Set Intersection, Trust Management
JOURNAL NAME:
Journal of Information Security,
Vol.13 No.1,
January
27,
2022
ABSTRACT: With the rapid development of intelligent transportation, carpooling with
the help of Vehicular Networks plays an important role in improving
transportation efficiency and solving
environmental problems. However, attackers usually 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 privacy-preserving carpooling matching scheme in
Vehicular Networks (TPCM). TPCM
scheme introduced travel preferences during carpooling matching, according to
the passengers’ individual travel preferences needs, which adopted the privacy set intersection technology based on the
Bloom filter to match the passengers with the vehicles to achieve the
purpose of protecting privacy and meeting
the individual needs of passengers simultaneously. TPCM scheme adopted a
multi-faceted trust management model, which calculated the trust value of different travel preferences of vehicle
based on passengers’ carpooling 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.