一种新的双层多机密共享方案

Elavarasi Gunasekaran, Vanitha Muthuraman
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引用次数: 0

摘要

加密技术被认为是保护数据隐私的最佳策略,在共享数据之前,会提前对数据进行编码。可视化秘密共享(VSS)是一种加密方法,在这种方法中,秘密信息被分割成至少两个微不足道的图像(称为 "共享")来覆盖。然而,黑客或不诚实的成员总是会瞄准这种信息,试图解密信息。要避免这种情况,就不能在没有通用共享的情况下揭露秘密信息,因为这种情况通常是由受信任的一方处理的。因此,本文提出了一种最佳和安全的双层秘密图像共享方案。本文提出的共享创建过程包含两层,第一层是基于阈值的秘密共享,第二层是基于通用共享的秘密共享。在第一层,应用遗传算法(GA)根据创建共享的随机性找到最佳阈值。然后,在第二层,提出了一种基于通用共享的秘密共享创建方法的新设计。最后,生成基于对立鲸优化算法(OWOA)的最优密钥,用于矩形块密码,以确保每个份额的安全。这有助于生成高质量的重建图像。研究人员在 PSNR 和 MSE 值方面取得了平均实验结果,分别为 55.154225 和 0.79365625。而现有方法的平均 PSNR 值较低(49.134475),平均 MSE 值较高(I)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new double layer multi-secret sharing scheme
Cryptography is deemed to be the optimum strategy to secure the data privacy in which the data is encoded ahead of time before sharing it. Visual Secret Sharing (VSS) is an encryption method in which the secret message is split into at least two trivial images called ‘shares’ to cover it. However, such message are always targeted by hackers or dishonest members who attempt to decrypt the message. This can be avoided by not uncovering the secret message without the universal share when it is presented and is typically taken care of, by the trusted party. Hence, in this paper, an optimal and secure double-layered secret image sharing scheme is proposed. The proposed share creation process contains two layers such as threshold-based secret sharing in the first layer and universal share based secret sharing in the second layer. In first layer, Genetic Algorithm (GA)is applied to find the optimal threshold value based on the randomness of the created shares. Then, in the second layer, a novel design of universal share-based secret share creation method is proposed. Finally, Opposition Whale Optimization Algorithm (OWOA)-based optimal key was generated for rectange block cipher to secure each share. This helped in producing high quality reconstruction images. The researcher achieved average experimental outcomes in terms of PSNR and MSE values equal to 55.154225 and 0.79365625 respectively. The average PSNRwas less (49.134475) and average MSE was high (I) in case of existing methods.
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