A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks
In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, the authors provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. The authors first describe the related definitions and the current state of homomorphic encryption. Next, the authors present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, the authors describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
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Supplemental Notes:
- © 2020 Xiaoqiang Sun et al.
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Authors:
- Sun, Xiaoqiang
- Yu, F Richard
- Zhang, Peng
- Xie, Weixin
- Peng, Xiang
- Publication Date: 2020-8
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 4253
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Serial:
- Sensors
- Volume: 20
- Issue Number: 15
- Publisher: MDPI AG
- ISSN: 1424-8220
- Serial URL: http://www.mdpi.com/journal/sensors
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Computer security; Data analysis; Mobile communication systems; Surveys; Vehicular ad hoc networks; Wireless communication systems
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01748699
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 27 2020 9:59AM