An efficient fuzzy certificateless signature-based authentication scheme using anonymous biometric identities for VANETs

生物识别 签名(拓扑) 计算机科学 认证(法律) 方案(数学) 计算机安全 模糊逻辑 数学 人工智能 数学分析 几何学
作者
Liangliang Wang,Jiangwei Xu,Baodong Qin,Mi Wen,Kefei Chen
出处
期刊:IEEE Transactions on Dependable and Secure Computing [IEEE Computer Society]
卷期号:: 1-16 被引量:4
标识
DOI:10.1109/tdsc.2024.3392470
摘要

Vehicular ad hoc networks (VANETs) are essential technologies to ensure safe road traffic management and enhance driving convenience. Nowadays, diversified authentication schemes have been developed in VANETs for the purpose of safer communication between nodes. For instance, biometric technology which employs biometric information as users' authentic identity is widely adopted in message authentication due to its visible benefits. Nonetheless, there is a significant problem in current biometric identity-based authentication schemes that noise is inevitable in each collection of biometric information, making these schemes lack critical error tolerance. Additionally, anonymous biometric identity is difficult to be realized, which fails to meet the basic standard of VANETs. For solving the above key issues, we propose the first efficient fuzzy certificateless signature-based (FCLS) authentication scheme using anonymous biometric identities for VANETs. In virtue of its superior error tolerance, it enables authentication between two identities represented by two attribute sets within a certain Hamming distance. Besides, the newly developed authentication scheme realizes effective conditional privacy so that drivers' real biometric identities can be ensured. Through the formal security proof, this FCLS scheme is existentially unforgeable against adaptive chosen message attack (EU-CMA) in the random oracle model (ROM), which reaches the higher security. Compared with current advanced schemes, the new authentication scheme is more efficient in computation and communication according to performance analysis.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
Yoci发布了新的文献求助10
1秒前
2秒前
檀艺发布了新的文献求助10
2秒前
2秒前
英姑应助健壮的凝安采纳,获得10
3秒前
落后鸭子发布了新的文献求助10
3秒前
4秒前
4秒前
快乐紫萱发布了新的文献求助10
5秒前
5秒前
沉默不评完成签到,获得积分10
5秒前
5秒前
刘mou发布了新的文献求助10
6秒前
7秒前
小宇完成签到,获得积分10
7秒前
板栗发布了新的文献求助10
7秒前
粗心的懿轩完成签到 ,获得积分10
7秒前
siwei发布了新的文献求助10
9秒前
9秒前
@@@发布了新的文献求助10
9秒前
深情安青应助Yoci采纳,获得10
12秒前
12秒前
无语的幻露完成签到,获得积分10
13秒前
14秒前
14秒前
14秒前
叶言完成签到,获得积分10
14秒前
15秒前
Jarch完成签到,获得积分10
15秒前
123456发布了新的文献求助20
15秒前
zho驳回了赘婿应助
17秒前
wanci应助仔仔采纳,获得10
17秒前
Akim应助起风采纳,获得10
18秒前
19秒前
123完成签到,获得积分10
19秒前
孙嘉俊完成签到,获得积分10
19秒前
CipherSage应助Pran采纳,获得10
19秒前
19秒前
20秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Molecular Mechanisms of Photosynthesis, 4th Edition 1000
Organic Reactions, Volume 116 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7256078
求助须知:如何正确求助?哪些是违规求助? 8878104
关于积分的说明 18750117
捐赠科研通 6936231
什么是DOI,文献DOI怎么找? 3200653
关于科研通互助平台的介绍 2374963
邀请新用户注册赠送积分活动 2176175