• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

MSD:用于数据隐私保护的多阶段欺骗

MSD: Multi-stage deception for data privacy protection.

作者信息

Abdel Latif Ali Tamer, Elsherbini Mostafa M

机构信息

Computer Science Department, Arab Academy for Science, Technology and Maritime Transport, Aswan, Egypt.

Department of Software Engineering, Arab Academy for Science, Technology & Maritime Transport, Aswan, Egypt.

出版信息

PLoS One. 2025 Jun 2;20(6):e0323944. doi: 10.1371/journal.pone.0323944. eCollection 2025.

DOI:10.1371/journal.pone.0323944
PMID:40455737
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12129173/
Abstract

With the exponential growth of electronically transmitted and stored data, ensuring data privacy and security has become a fundamental challenge for organizations and enterprises. Traditional encryption methods have limitations, such as vulnerability to advanced attacks and high computational complexity, that lead to the exploration of complementary strategies like deception techniques for enhanced protection. These methods aim to mislead unauthorized users by presenting protected data as if it were authentic, but the attack resilience is still insufficient. Multi-stage deception (MSD) methods leverage multiple deception strategies, such as complement, swapping, and stack reversal, to improve data protection levels and resistance against decryption attempts. Combining these techniques addresses gaps in single-stage approaches and offers a more robust defense. The proposed MSD method incorporates a classification of encryption and deception techniques and introduces a novel evaluation approach targeting critical performance factors. A tailored pseudocode algorithm is designed to optimize deception for various attribute types, validated through simulations. Simulation results reveal that the MSD method achieves a [Formula: see text] value change in the first stage and [Formula: see text] in the second stage, with an overall accuracy exceeding [Formula: see text]. These findings demonstrate the method's effectiveness in elevating data protection levels while maintaining low computational complexity. The study highlights the potential of multi-stage deception as a powerful tool for safeguarding sensitive information, achieving superior performance in data security. By offering a scalable and adaptable framework, the MSD method addresses emerging challenges in data protection while setting the stage for further advancements.

摘要

随着电子传输和存储数据的指数级增长,确保数据隐私和安全已成为组织和企业面临的一项基本挑战。传统加密方法存在局限性,如易受高级攻击且计算复杂度高,这促使人们探索诸如欺骗技术等互补策略以加强保护。这些方法旨在通过将受保护数据呈现为真实数据来误导未经授权的用户,但抗攻击能力仍然不足。多阶段欺骗(MSD)方法利用多种欺骗策略,如互补、交换和堆栈反转,来提高数据保护级别和抵御解密尝试的能力。结合这些技术可弥补单阶段方法的不足,并提供更强大的防御。所提出的MSD方法纳入了加密和欺骗技术的分类,并引入了一种针对关键性能因素的新颖评估方法。设计了一种定制的伪代码算法,以针对各种属性类型优化欺骗,并通过模拟进行了验证。模拟结果表明,MSD方法在第一阶段实现了[公式:见文本]的价值变化,在第二阶段实现了[公式:见文本]的变化,总体准确率超过[公式:见文本]。这些发现证明了该方法在提高数据保护级别同时保持低计算复杂度方面的有效性。该研究突出了多阶段欺骗作为保护敏感信息的强大工具的潜力,在数据安全方面实现了卓越性能。通过提供一个可扩展且适应性强的框架,MSD方法应对了数据保护中出现的挑战,同时为进一步发展奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/5c62f083f62c/pone.0323944.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/5c7cb4f61440/pone.0323944.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/30811dee0fe3/pone.0323944.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/5a9bf8a69066/pone.0323944.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/4c492908bd65/pone.0323944.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/6ccb8537f895/pone.0323944.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/6b1c3165dbc3/pone.0323944.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/1e74e228ae81/pone.0323944.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/a307faccce61/pone.0323944.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/7b440176e360/pone.0323944.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/5c62f083f62c/pone.0323944.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/5c7cb4f61440/pone.0323944.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/30811dee0fe3/pone.0323944.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/5a9bf8a69066/pone.0323944.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/4c492908bd65/pone.0323944.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/6ccb8537f895/pone.0323944.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/6b1c3165dbc3/pone.0323944.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/1e74e228ae81/pone.0323944.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/a307faccce61/pone.0323944.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/7b440176e360/pone.0323944.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5272/12129173/5c62f083f62c/pone.0323944.g010.jpg

相似文献

1
MSD: Multi-stage deception for data privacy protection.MSD:用于数据隐私保护的多阶段欺骗
PLoS One. 2025 Jun 2;20(6):e0323944. doi: 10.1371/journal.pone.0323944. eCollection 2025.
2
Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain.基于深度学习的医学图像加密方案:使用深度卷积生成对抗网络和虚拟星球域
Sci Rep. 2025 Jan 7;15(1):1211. doi: 10.1038/s41598-024-84186-6.
3
Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products.基于区块链的农产品生物风险隐私保护代理重加密访问控制方法。
Sci Rep. 2024 Aug 29;14(1):20048. doi: 10.1038/s41598-024-70533-0.
4
Securing healthcare data: A federated learning framework with hybrid encryption in cluster environments.保护医疗保健数据:一种在集群环境中采用混合加密的联邦学习框架。
Technol Health Care. 2025 May;33(3):1232-1257. doi: 10.1177/09287329241291397. Epub 2024 Nov 25.
5
A bidirectional reversible and multilevel location privacy protection method based on attribute encryption.一种基于属性加密的双向可逆多层次位置隐私保护方法。
PLoS One. 2024 Sep 6;19(9):e0309990. doi: 10.1371/journal.pone.0309990. eCollection 2024.
6
Chaotic medical image encryption method using attention mechanism fusion ResNet model.基于注意力机制融合ResNet模型的混沌医学图像加密方法
Front Neurosci. 2023 Jul 13;17:1226154. doi: 10.3389/fnins.2023.1226154. eCollection 2023.
7
HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications.HealthLock:物联网医疗应用中基于同态加密的区块链隐私保护
Sensors (Basel). 2023 Jul 28;23(15):6762. doi: 10.3390/s23156762.
8
A double encryption protection algorithm for stem cell bank privacy data based on improved AES and chaotic encryption technology.基于改进 AES 和混沌加密技术的干细胞库隐私数据双重加密保护算法。
PLoS One. 2023 Oct 25;18(10):e0293418. doi: 10.1371/journal.pone.0293418. eCollection 2023.
9
Blockchain-enabled data governance for privacy-preserved sharing of confidential data.支持区块链的数据治理,用于在保护隐私的前提下共享机密数据。
PeerJ Comput Sci. 2024 Dec 20;10:e2581. doi: 10.7717/peerj-cs.2581. eCollection 2024.
10
Privacy Preservation in Patient Information Exchange Systems Based on Blockchain: System Design Study.基于区块链的患者信息交换系统中的隐私保护:系统设计研究。
J Med Internet Res. 2022 Mar 22;24(3):e29108. doi: 10.2196/29108.

本文引用的文献

1
EU space security-An 8-Step online discourse analysis to decode hybrid threats.欧盟空间安全——一种用于解码混合威胁的 8 步在线话语分析方法。
PLoS One. 2024 Jul 15;19(7):e0303524. doi: 10.1371/journal.pone.0303524. eCollection 2024.
2
Towards secure and efficient integration of blockchain and 6G networks.实现区块链与 6G 网络的安全高效融合。
PLoS One. 2024 Apr 11;19(4):e0302052. doi: 10.1371/journal.pone.0302052. eCollection 2024.
3
Deception detection with machine learning: A systematic review and statistical analysis.使用机器学习进行欺骗检测:系统评价和统计分析。
PLoS One. 2023 Feb 9;18(2):e0281323. doi: 10.1371/journal.pone.0281323. eCollection 2023.
4
An Innovative Method for Preserving Privacy in Internet of Things.一种物联网中保护隐私的创新方法。
Sensors (Basel). 2019 Jul 31;19(15):3355. doi: 10.3390/s19153355.