基于轻量级密码学的生物特征图像加密多秘密共享创建

Elavarasi Gunasekaran, Vanitha Muthuraman
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引用次数: 0

摘要

由于信息技术的快速发展,对网络安全和生物识别技术的需求日益增长。生物识别图像保护是一个重要问题,因为数字图像和医疗细节是通过公共网络分发的。本研究工作提出了一种基于阈值的生物特征图像共享创建方案。为了提高股票的安全级别,每个股票都采用轻量级加密(LWC)-流密码方法进行加密。为了提高流密码的加密效率,采用蚂蚁优化算法(ALO)选择最优密钥。使用流密码的好处是执行速度比块密码最高,并且不那么复杂。所建议的流密码方法的好处是,对密钥流中的密钥和明文中的字符的解码表示解密的生物特征图像将提高设备的可靠性。从实现结果来看,与其他现有技术相比,该模型在生物特征图像安全的情况下实现了最大的PSNR。
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Light Weight Cryptography Based Encrypted Multiple Secret Share Creation for Biometrics Images
Owing to the rapid growth of information technologies, a rising need for cybersecurity and biometric technologies is increasingly evolving. Biometrics image protection is an important problem as digital images and medical details are distributed via public networks. This research work proposed a threshold-based share creation scheme for Biometrics images. To enhance the security level of the shares, each shares are encrypted by Light Weight Cryptography (LWC)-Stream Cipher method. To increase the stream cipher encryption efficiency, optimal keys are selected by Ant Lion Optimization (ALO) technique. The benefit of consuming stream ciphers is that the speed of execution is maximum over block cipher and less complex. The benefit of the suggested stream cipher approach is that the decoding of the keys in the keystream and the characters in the plain text denotes decrypted biometrics image will improve device reliability. From the implementation results proposed model achieves the maximum PSNR with the security of Biometrics images, compared to other existing techniques.
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来源期刊
Journal of Computational and Theoretical Nanoscience
Journal of Computational and Theoretical Nanoscience 工程技术-材料科学:综合
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3.9 months
期刊介绍: Information not localized
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