基于神经密码编码的增强不可信云环境下图像安全性的方法

Pallavi Kulkarni, Rajashri Khanai, Dattaprasad Torse, N. Iyer, Gururaj Bindagi
{"title":"基于神经密码编码的增强不可信云环境下图像安全性的方法","authors":"Pallavi Kulkarni, Rajashri Khanai, Dattaprasad Torse, N. Iyer, Gururaj Bindagi","doi":"10.3390/cryptography7020023","DOIUrl":null,"url":null,"abstract":"The cloud provides on-demand, high-quality services to its users without the burden of managing hardware and software. Though the users benefit from the remote services provided by the cloud, they do not have their personal data in their physical possession. This certainly poses new security threats for personal and confidential data, bringing the focus back on trusting the use of the cloud for sensitive data. The benefits of the cloud outweigh the concerns raised earlier, and with an increase in cloud usage, it becomes more important for security services to evolve in order to address the ever-changing threat landscape. Advanced encryption standard (AES), being one of the most widely used encryption techniques, has inherent disadvantages related to the secret key that is shared, and predictable patterns in subkey generation. In addition, since cloud storage involves data transfer over a wireless channel, it is important to address the effect of noise and multipath propagation on the transmitted data. Catering to this problem, we propose a new approach—the secure and reliable neural cryptcoding (SARNC) technique—which provides a superior algorithm, dealing with better encryption techniques combined with channel coding. A chain is as strong as the weakest link and, in the case of symmetric key encryption, the weakest link is the shared key. In order to overcome this limitation, we propose an approach wherein the key used for cryptographic purposes is different from the key shared between the sender and the receiver. The shared key is used to derive the secret private key, which is generated by the neural key exchange protocol. In addition, the proposed approach emphasizes strengthening the sub-key generation process and integrating advanced encryption standard (AES) with low-density parity check (LDPC) codes to provide end-to-end security and reliability over wireless channels. The proposed technique was tested against research done in related areas. A comparative study shows a significant improvement in PSNR, MSE, and the structural similarity index (SSIM). The key strength analysis was carried out to understand the strength and weaknesses of the keys generated.","PeriodicalId":13186,"journal":{"name":"IACR Trans. Cryptogr. Hardw. Embed. Syst.","volume":"116 1","pages":"23"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Neural Crypto-Coding Based Approach to Enhance the Security of Images over the Untrusted Cloud Environment\",\"authors\":\"Pallavi Kulkarni, Rajashri Khanai, Dattaprasad Torse, N. Iyer, Gururaj Bindagi\",\"doi\":\"10.3390/cryptography7020023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The cloud provides on-demand, high-quality services to its users without the burden of managing hardware and software. Though the users benefit from the remote services provided by the cloud, they do not have their personal data in their physical possession. This certainly poses new security threats for personal and confidential data, bringing the focus back on trusting the use of the cloud for sensitive data. The benefits of the cloud outweigh the concerns raised earlier, and with an increase in cloud usage, it becomes more important for security services to evolve in order to address the ever-changing threat landscape. Advanced encryption standard (AES), being one of the most widely used encryption techniques, has inherent disadvantages related to the secret key that is shared, and predictable patterns in subkey generation. In addition, since cloud storage involves data transfer over a wireless channel, it is important to address the effect of noise and multipath propagation on the transmitted data. Catering to this problem, we propose a new approach—the secure and reliable neural cryptcoding (SARNC) technique—which provides a superior algorithm, dealing with better encryption techniques combined with channel coding. A chain is as strong as the weakest link and, in the case of symmetric key encryption, the weakest link is the shared key. In order to overcome this limitation, we propose an approach wherein the key used for cryptographic purposes is different from the key shared between the sender and the receiver. The shared key is used to derive the secret private key, which is generated by the neural key exchange protocol. In addition, the proposed approach emphasizes strengthening the sub-key generation process and integrating advanced encryption standard (AES) with low-density parity check (LDPC) codes to provide end-to-end security and reliability over wireless channels. The proposed technique was tested against research done in related areas. A comparative study shows a significant improvement in PSNR, MSE, and the structural similarity index (SSIM). The key strength analysis was carried out to understand the strength and weaknesses of the keys generated.\",\"PeriodicalId\":13186,\"journal\":{\"name\":\"IACR Trans. Cryptogr. Hardw. Embed. Syst.\",\"volume\":\"116 1\",\"pages\":\"23\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IACR Trans. Cryptogr. Hardw. Embed. Syst.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/cryptography7020023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IACR Trans. Cryptogr. Hardw. Embed. Syst.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/cryptography7020023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

云为用户提供按需、高质量的服务,而无需管理硬件和软件。尽管用户受益于云提供的远程服务,但他们并没有实际拥有自己的个人数据。这无疑给个人和机密数据带来了新的安全威胁,使人们重新关注对敏感数据使用云的信任。云计算的好处超过了之前提出的担忧,随着云使用的增加,为了应对不断变化的威胁环境,安全服务的发展变得更加重要。高级加密标准(AES)是使用最广泛的加密技术之一,它在共享密钥和子密钥生成的可预测模式方面存在固有的缺点。此外,由于云存储涉及通过无线信道传输数据,因此解决噪声和多径传播对传输数据的影响非常重要。针对这个问题,我们提出了一种新的方法——安全可靠的神经密码编码(SARNC)技术,它提供了一种优越的算法,可以处理与信道编码相结合的更好的加密技术。一条链的强度与最弱的环节一样大,在对称密钥加密的情况下,最弱的环节是共享密钥。为了克服这一限制,我们提出了一种方法,其中用于加密目的的密钥与发送方和接收方之间共享的密钥不同。使用共享密钥派生秘密私钥,由神经密钥交换协议生成。此外,该方法强调加强子密钥生成过程,并将高级加密标准(AES)与低密度奇偶校验(LDPC)码集成在一起,以提供无线信道上的端到端安全性和可靠性。所提出的技术经过了相关领域研究的检验。对比研究表明,PSNR、MSE和结构相似指数(SSIM)均有显著改善。进行密钥强度分析,了解生成的密钥的优缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Crypto-Coding Based Approach to Enhance the Security of Images over the Untrusted Cloud Environment
The cloud provides on-demand, high-quality services to its users without the burden of managing hardware and software. Though the users benefit from the remote services provided by the cloud, they do not have their personal data in their physical possession. This certainly poses new security threats for personal and confidential data, bringing the focus back on trusting the use of the cloud for sensitive data. The benefits of the cloud outweigh the concerns raised earlier, and with an increase in cloud usage, it becomes more important for security services to evolve in order to address the ever-changing threat landscape. Advanced encryption standard (AES), being one of the most widely used encryption techniques, has inherent disadvantages related to the secret key that is shared, and predictable patterns in subkey generation. In addition, since cloud storage involves data transfer over a wireless channel, it is important to address the effect of noise and multipath propagation on the transmitted data. Catering to this problem, we propose a new approach—the secure and reliable neural cryptcoding (SARNC) technique—which provides a superior algorithm, dealing with better encryption techniques combined with channel coding. A chain is as strong as the weakest link and, in the case of symmetric key encryption, the weakest link is the shared key. In order to overcome this limitation, we propose an approach wherein the key used for cryptographic purposes is different from the key shared between the sender and the receiver. The shared key is used to derive the secret private key, which is generated by the neural key exchange protocol. In addition, the proposed approach emphasizes strengthening the sub-key generation process and integrating advanced encryption standard (AES) with low-density parity check (LDPC) codes to provide end-to-end security and reliability over wireless channels. The proposed technique was tested against research done in related areas. A comparative study shows a significant improvement in PSNR, MSE, and the structural similarity index (SSIM). The key strength analysis was carried out to understand the strength and weaknesses of the keys generated.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信