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Masking and Demasking Agents

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...

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Updated: Jun 12, 2025

In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity

In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity

Published on: March 25, 2020

一个基于MLWE的Cut-and-Choose忘记转移协议.

Yongli Tang¹, Menghao Guo², Yachao Huo³

¹School of Software, Henan Polytechnic University, Jiaozuo 454000, China.

Entropy (Basel, Switzerland)

|September 27, 2024

概括

本研究引入了使用模块学习与错误 (MLWE) 问题的高效无意识传输协议,提高了安全性,并减少了与现有的基于格子的方法相比的计算和通信开销.

关键词:

切割和选择可以选择.两种模式的加密.通过错误学习学习.遗忘的转移转移遗忘的转移量子攻击是一种量子攻击.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

相关实验视频

Last Updated: Jun 12, 2025

In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity

In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity

Published on: March 25, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

科学领域:

密码学和信息安全信息安全
基于格子的加密技术
量子电阻密码学量子电阻密码学

背景情况:

现有的基于格子的切换和选择的无意识传输协议往往由于依赖错误学习 (LWE) 问题而遭受效率低下.
开发更高效,更安全的加密协议对于现代数据保护至关重要.

研究的目的:

提出一个高效的切割和选择的无意识传输协议.
为了提高安全性,减少无意识传输协议中的计算和通信复杂性.

主要方法:

开发了一种基于模块学习与错误 (MLWE) 问题的新方案,这是一个更困难的格子问题.
将压缩和解压缩技术集成到基于LWE的双模式加密系统中,以创建基于MLWE的框架.
利用快速里叶变换 (FFT) 来优化多项式模数乘法运算.

主要成果:

拟议的协议证明了增强的安全性,可归结为格子上最短的独立矢量问题 (SIVP),提供对量子攻击的抵抗力.
实现了显著的效率提高,至少减少了"n"的计算和通信开销.
该协议即使在恶意对手的攻击下也保持最佳的通信回合.

结论:

基于MLWE的隐形传输协议为现有的基于LWE的协议提供了更有效和更安全的替代方案.
使用MLWE,压缩/解压缩和FFT等先进技术,有助于提供强大的高性能加密解决方案.
这项研究推动了抗量子密码学领域的发展,为安全通信提供了实际改进.