TY - JOUR
T1 - Location and trajectory privacy preservation in 5G-Enabled vehicle social network services
AU - Liao, Dan
AU - Li, Hui
AU - Sun, Gang
AU - Zhang, Ming
AU - Chang, Victor
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/5/15
Y1 - 2018/5/15
N2 - 5G-based Vehicular Social Networks (VSNs) demand an advanced location and trajectory privacy preserving scheme for vehicles. Because VSNs present the characteristics of high mobility and multiple hop relays, we design a 5G-based VSN framework that incorporates Mobile Femtocell (MFemtocell) technology. Then, we propose the Dynamic Group Division algorithm (DGD), which is suitable for the dynamic properties of 5G and meets the real-time demands of VSN. To preserve privacy, the DGD algorithm increases the likelihood of exchanging pseudonyms via the proposed Group Generating Protocol and Pseudonym Exchanging Protocol. Then, we adopt the composite metric KDT (where K is the average anonymity set size, D is the average distance deviation, and T is the anonymity duration) and pseudonym entropy to quantify the degree of privacy. We evaluate and validate the effectiveness of our proposed algorithm based on the following three aspects: anonymity set size, distance deviation and pseudonym entropy. The simulation results show that our DGD algorithm better protects the location and trajectory privacy of VSNs while sustaining higher real-time demand than current approaches.
AB - 5G-based Vehicular Social Networks (VSNs) demand an advanced location and trajectory privacy preserving scheme for vehicles. Because VSNs present the characteristics of high mobility and multiple hop relays, we design a 5G-based VSN framework that incorporates Mobile Femtocell (MFemtocell) technology. Then, we propose the Dynamic Group Division algorithm (DGD), which is suitable for the dynamic properties of 5G and meets the real-time demands of VSN. To preserve privacy, the DGD algorithm increases the likelihood of exchanging pseudonyms via the proposed Group Generating Protocol and Pseudonym Exchanging Protocol. Then, we adopt the composite metric KDT (where K is the average anonymity set size, D is the average distance deviation, and T is the anonymity duration) and pseudonym entropy to quantify the degree of privacy. We evaluate and validate the effectiveness of our proposed algorithm based on the following three aspects: anonymity set size, distance deviation and pseudonym entropy. The simulation results show that our DGD algorithm better protects the location and trajectory privacy of VSNs while sustaining higher real-time demand than current approaches.
KW - 5G
KW - Location privacy
KW - Mobile femtocell
KW - Trajectory privacy
KW - VSN
UR - http://www.scopus.com/inward/record.url?scp=85042021175&partnerID=8YFLogxK
U2 - 10.1016/j.jnca.2018.02.002
DO - 10.1016/j.jnca.2018.02.002
M3 - Article
AN - SCOPUS:85042021175
SN - 1084-8045
VL - 110
SP - 108
EP - 118
JO - Journal of Network and Computer Applications
JF - Journal of Network and Computer Applications
ER -