Key derivation function: key-hash based computational extractor and stream based pseudorandom expander

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE PeerJ Computer Science Pub Date : 2024-08-23 DOI:10.7717/peerj-cs.2249
Chai Wen Chuah, Nur Ziadah Harun, Isredza Rahmi A. Hamid
{"title":"Key derivation function: key-hash based computational extractor and stream based pseudorandom expander","authors":"Chai Wen Chuah, Nur Ziadah Harun, Isredza Rahmi A. Hamid","doi":"10.7717/peerj-cs.2249","DOIUrl":null,"url":null,"abstract":"The key derivation function is a specific cryptographic algorithm that transforms private string and public strings into one or more cryptographic keys. The cryptographic keys are essential for protecting electronic data during transmission on the internet. This function is designed based on a computational extractor and pseudorandom expander and is typically constructed using various cryptography ciphers such as stream ciphers, keyed-hash message authentication codes, and block ciphers. Having secure and efficient key derivation function designs is essential in the development of numerous security systems. A vulnerable key derivation function could potentially give attackers the ability to compromise an otherwise secure cryptosystem. This research proposes a different approach by combining two different cryptography ciphers to develop key derivation functions. The findings demonstrate that a computational extractor utilizing keyed-hash message authentication codes and a pseudorandom expander using stream ciphers maintain the highest level of security while also providing efficiency benefits in terms of execution time compared to existing key derivation function schemes.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"1 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PeerJ Computer Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.7717/peerj-cs.2249","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

The key derivation function is a specific cryptographic algorithm that transforms private string and public strings into one or more cryptographic keys. The cryptographic keys are essential for protecting electronic data during transmission on the internet. This function is designed based on a computational extractor and pseudorandom expander and is typically constructed using various cryptography ciphers such as stream ciphers, keyed-hash message authentication codes, and block ciphers. Having secure and efficient key derivation function designs is essential in the development of numerous security systems. A vulnerable key derivation function could potentially give attackers the ability to compromise an otherwise secure cryptosystem. This research proposes a different approach by combining two different cryptography ciphers to develop key derivation functions. The findings demonstrate that a computational extractor utilizing keyed-hash message authentication codes and a pseudorandom expander using stream ciphers maintain the highest level of security while also providing efficiency benefits in terms of execution time compared to existing key derivation function schemes.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
密钥衍生功能:基于密钥哈希值的计算提取器和基于流的伪随机扩展器
密钥推导函数是一种特定的加密算法,可将私人字符串和公共字符串转换成一个或多个加密密钥。加密密钥对于保护互联网传输过程中的电子数据至关重要。该功能基于计算提取器和伪随机扩展器设计,通常使用流密码、密钥哈希信息验证码和块密码等各种密码学密码来构建。安全高效的密钥推导函数设计对于众多安全系统的开发至关重要。易受攻击的密钥推导函数有可能使攻击者有能力破坏原本安全的密码系统。这项研究提出了一种不同的方法,即结合两种不同的密码学密码来开发密钥导出函数。研究结果表明,与现有的密钥推导函数方案相比,利用密钥哈希信息验证码的计算提取器和利用流密码的伪随机扩展器既能保持最高级别的安全性,又能在执行时间方面提高效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
期刊最新文献
A model integrating attention mechanism and generative adversarial network for image style transfer. Detecting rumors in social media using emotion based deep learning approach. Harnessing AI and analytics to enhance cybersecurity and privacy for collective intelligence systems. Improving synthetic media generation and detection using generative adversarial networks. Intelligent accounting optimization method based on meta-heuristic algorithm and CNN.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1