TY - JOUR
T1 - Privacy-Preserving Content Dissemination for Vehicular Social Networks
T2 - Challenges and Solutions
AU - Wang, Xiaojie
AU - Ning, Zhaolong
AU - Zhou, Meng Chu
AU - Hu, Xiping
AU - Wang, Lei
AU - Zhang, Yan
AU - Yu, Fei Richard
AU - Hu, Bin
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61733002, Grant 61502075, Grant 61632014, Grant 61772508, and Grant 81401570, in part by the National Basic Research Program of China under Grant 2014CB744600, in part by the Fundamental Research Funds for the Central University under Grant DUT17LAB16, Grant DUT2017TB02, and Grant DUT17RC(4)49, in part by China Postdoctoral Science Foundation under Grant 2018T110210, in part by the ShenzhenHong Kong Innovative Project under Grant SGLH20161212140718841, in part by the Guangdong Technology Project under Grant 2016B010108010, Grant 2016B010125003, and Grant 2017B010110007, in part by the Shenzhen Technology Project under Grant JCYJ20170413152535587, Grant JSGG20160331185256983, and Grant JSGG20160229115709109, in part by the Tianjin Key Laboratory of Advanced Networking, and in part by the School of Computer Science and Technology, Tianjin University, Tianjin, China.
Funding Information:
Manuscript received March 12, 2018; revised August 10, 2018 and October 6, 2018; accepted November 9, 2018. Date of publication November 19, 2018; date of current version May 31, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61733002, Grant 61502075, Grant 61632014, Grant 61772508, and Grant 81401570, in part by the National Basic Research Program of China under Grant 2014CB744600, in part by the Fundamental Research Funds for the Central University under Grant DUT17LAB16, Grant DUT2017TB02, and Grant DUT17RC(4)49, in part by China Postdoctoral Science Foundation under Grant 2018T110210, in part by the Shenzhen–Hong Kong Innovative Project under Grant SGLH20161212140718841, in part by the Guangdong Technology Project under Grant 2016B010108010, Grant 2016B010125003, and Grant 2017B010110007, in part by the Shenzhen Technology Project under Grant JCYJ20170413152535587, Grant JSGG20160331185256983, and Grant JSGG20160229115709109, in part by the Tianjin Key Laboratory of Advanced Networking, and in part by the School of Computer Science and Technology, Tianjin University, Tianjin, China. (Corresponding authors: Zhaolong Ning; Xiping Hu; Lei Wang; Bin Hu.) X. Wang and L. Wang are with the Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian 116620, China (e-mail: lei.wang@dlut.edu.cn).
Publisher Copyright:
© 1998-2012 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Vehicular social networks (VSNs), viewed as the integration of traditional vehicular networks and social networks, are promising communication platforms based on the development of intelligent vehicles and deployment of intelligent transportation systems. Passengers can obtain information by searching over Internet or querying vehicles in proximity through intra-vehicle equipment. Hence, the performance of content dissemination in VSNs heavily relies on inter-vehicle communication and human behaviors. However, privacy preservation always conflicts with the usability of individual information in VSNs. The highly dynamic topology and increasing kinds of participants lead to potential threats for communication security and individual privacy. Therefore, the privacy-preserving solutions for content dissemination in VSNs have become extremely challenging, and numerous researches have been conducted recently. Compared with related surveys, this article provides the unique characteristics of privacy-preserving requirements and solutions for content dissemination in VSNs. It focuses on: 1) a comprehensive overview of content dissemination in VSNs; 2) the privacy issues and potential attacks related to content dissemination; and 3) the corresponding solutions based on privacy consideration. First, the characteristics of VSNs, content dissemination and its solutions in VSNs are revealed. Second, the privacy issues for content dissemination in the current VSN architecture are analyzed and classified according to their features. Various privacy-preserving content dissemination schemes, attempting to resist distinct attacks, are also discussed. Finally, the research challenges and open issues are summarized.
AB - Vehicular social networks (VSNs), viewed as the integration of traditional vehicular networks and social networks, are promising communication platforms based on the development of intelligent vehicles and deployment of intelligent transportation systems. Passengers can obtain information by searching over Internet or querying vehicles in proximity through intra-vehicle equipment. Hence, the performance of content dissemination in VSNs heavily relies on inter-vehicle communication and human behaviors. However, privacy preservation always conflicts with the usability of individual information in VSNs. The highly dynamic topology and increasing kinds of participants lead to potential threats for communication security and individual privacy. Therefore, the privacy-preserving solutions for content dissemination in VSNs have become extremely challenging, and numerous researches have been conducted recently. Compared with related surveys, this article provides the unique characteristics of privacy-preserving requirements and solutions for content dissemination in VSNs. It focuses on: 1) a comprehensive overview of content dissemination in VSNs; 2) the privacy issues and potential attacks related to content dissemination; and 3) the corresponding solutions based on privacy consideration. First, the characteristics of VSNs, content dissemination and its solutions in VSNs are revealed. Second, the privacy issues for content dissemination in the current VSN architecture are analyzed and classified according to their features. Various privacy-preserving content dissemination schemes, attempting to resist distinct attacks, are also discussed. Finally, the research challenges and open issues are summarized.
KW - Vehicular social networks
KW - attack resistance
KW - content dissemination
KW - individual privacy
KW - potential attacks
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U2 - 10.1109/COMST.2018.2882064
DO - 10.1109/COMST.2018.2882064
M3 - Review article
AN - SCOPUS:85056725801
SN - 1553-877X
VL - 21
SP - 1314
EP - 1345
JO - IEEE Communications Surveys and Tutorials
JF - IEEE Communications Surveys and Tutorials
IS - 2
M1 - 8539991
ER -