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基于格的Lenstra-Lenstra-Lovász算法-密文策略属性加密的分布式身份认证:一种基于环上误差学习问题的高效方法。

Distributed Identity Authentication with Lenstra-Lenstra-Lovász Algorithm-Ciphertext Policy Attribute-Based Encryption from Lattices: An Efficient Approach Based on Ring Learning with Errors Problem.

作者信息

Yuan Qi, Yuan Hao, Zhao Jing, Zhou Meitong, Shao Yue, Wang Yanchun, Zhao Shuo

机构信息

Faculty of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China.

State Grid Information Communication Branch, Beijing 100032, China.

出版信息

Entropy (Basel). 2024 Aug 27;26(9):729. doi: 10.3390/e26090729.

DOI:10.3390/e26090729
PMID:39330064
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11431376/
Abstract

In recent years, research on attribute-based encryption (ABE) has expanded into the quantum domain. Because a traditional single authority can cause the potential single point of failure, an improved lattice-based quantum-resistant identity authentication and policy attribute encryption scheme is proposed, in which the generation of random values is optimized by adjusting parameters in the Gaussian sampling algorithm to improve overall performance. Additionally, in the key generation phase, attributes are processed according to their shared nature, which reduces the computational overhead of the authorization authority. In the decryption phase, the basis transformation of the Lenstra-Lenstra-Lovász (LLL) lattice reduction algorithm is utilized to rapidly convert shared matrices into the shortest vector form, which can reduce the computational cost of linear space checks. The experimental results demonstrate that the proposed method not only improves efficiency but also enhances security compared with related schemes.

摘要

近年来,基于属性的加密(ABE)研究已扩展到量子领域。由于传统的单一授权机构可能导致潜在的单点故障,因此提出了一种改进的基于格的抗量子身份认证和策略属性加密方案,其中通过调整高斯采样算法中的参数来优化随机值的生成,以提高整体性能。此外,在密钥生成阶段,根据属性的共享性质对其进行处理,这减少了授权机构的计算开销。在解密阶段,利用Lenstra-Lenstra-Lovász(LLL)格规约算法的基变换将共享矩阵快速转换为最短向量形式,这可以降低线性空间检查的计算成本。实验结果表明,与相关方案相比,该方法不仅提高了效率,还增强了安全性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2230/11431376/78944bd304f2/entropy-26-00729-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2230/11431376/a0f9bc669e06/entropy-26-00729-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2230/11431376/7a41e2efbe21/entropy-26-00729-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2230/11431376/78944bd304f2/entropy-26-00729-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2230/11431376/a0f9bc669e06/entropy-26-00729-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2230/11431376/7a41e2efbe21/entropy-26-00729-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2230/11431376/78944bd304f2/entropy-26-00729-g003.jpg

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