基于超椭圆曲线和密码安全随机数的生物特征签名加密

K. Sathya, V. Rajasekar, J. Premalatha
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引用次数: 5

摘要

生物识别系统存储用户的生理特征,如指纹、虹膜、手掌、静脉图案等。虹膜签名加密是提高信息安全性的最成功的方法,因为与其他身份验证系统相比,虹膜包含了最准确的特征。为了提供更好的安全性和更少的计算时间,引入了一种称为签名加密的轻量级加密方案。签名加密是加密和签名的逻辑组合。采用加密安全随机数作为加密段的私钥和生成SHA-1段签名的密钥。通常,通过NIST测试套件的随机数不能满足加密安全要求。一种称为缠绕技术的方法与计数器模式下的AES一起用于从传感器的种子值生成加密随机数。缠绕技术是一种消除同一环境下多个传感器种子提取过程中共线性的新方法。超椭圆曲线密码术(HyperElliptic Curve cryptography, HECC)由于其使用的密钥尺寸更小,计算时间更短,是目前公钥加密领域研究的热点之一。该方法利用生物特征数据、HECC和加密安全随机数来推导虹膜的加密值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biometric signcryption using hyperelliptic curve and cryptographically secure random number
The biometric system stores physiological traits of user such as finger print, iris, palm, patterns of veins etc. Iris signcryption is the most successful way to improve the information security because iris consists of most accurate features compared to other authentication systems. In order to provide better security and less computational time lightweight cryptographic scheme called signcryption has introduced. Signcryption is a logical combination of encryption and signature. Cryptographically secure random numbers are used as private key in encryption segment and as key in generating signature of SHA-1 segment. Generally the random numbers passing the NIST Test suite fails to satisfy cryptographically secure requirements. An approach called twining technique along with AES in counter mode is used to generate cryptographically random numbers from the seed values of sensors. Twining technique is a novel method to minimize co linearity that occurs while extracting seed from multiple sensors housed in same environment. One of the most recent researches in the area of public key cryptography is HyperElliptic Curve Cryptography (HECC) because HECC uses smaller key size and less computational time. In the proposed biometric signcryption, biometric data, HECC and cryptographically secure random numbers are used to derive the signcrypted value of iris.
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