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利用集成变换后的 Paillier 和 KLEIN 算法加密技术与大象群优化算法，为医疗保健应用提供安全的医疗数据模型。

Secure Medical Data Model Using Integrated Transformed Paillier and KLEIN Algorithm Encryption Technique with Elephant Herd Optimization for Healthcare Applications.

机构信息

Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

出版信息

J Healthc Eng. 2022 Oct 25;2022:3991295. doi: 10.1155/2022/3991295. eCollection 2022.

DOI:10.1155/2022/3991295
PMID:36330360
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9626223/
Abstract

In the healthcare industry, where concerns are frequently and appropriately focused on saving someone's life, access to interfaces and computer systems storing sensitive data, such as medical records, is crucial to take into account. Medical information has to be secretive and protected by the laws of privacy with restrictions on its access. E-health security is a holistic notion that encompasses available medical data's integrities and confidentiality which ensures that data are not accessed by unauthorized people and allow doctors to offer proper treatment. The patients' data need to be secured on servers holding medical data. This work adds new features for ensuring storage and access safety through ITPKLEIN-EHO (integrated transformed Paillier and KLEIN algorithms) that use EHOs (elephant herd optimizations) to provide lightweight features. The key space affects lightweight encryption techniques in general. The EHOs (elephant herd optimizations) optimize key spaces by adjusting iteration rounds. The main goal is to encrypt EEGs (electroencephalographic signals) in healthcare and send it to end users using the proposed ITPKLEIN-EHO approach. This suggested technique utilizes MATLAB for its tests on various EEG data sets for implementation. The simulations of the proposed IRPKLEIN-EHO technique are evaluated with other existing techniques in terms of MSEs, PSNRs, SSIMs, PRDs, and encryption/decryption times.

摘要

在医疗保健行业，人们经常关注拯救生命，因此需要考虑访问存储敏感数据（如医疗记录）的接口和计算机系统。医疗信息必须保密，并受到隐私法规的保护，限制其访问。电子健康安全是一个整体概念，包括可用医疗数据的完整性和保密性，以确保数据不会被未经授权的人访问，并允许医生提供适当的治疗。患者的数据需要在存储医疗数据的服务器上进行保护。这项工作通过使用 EHO（大象群优化）的 ITPKLEIN-EHO（集成变换的 Paillier 和 KLEIN 算法）添加了新的功能，以确保存储和访问安全，提供轻量级功能。密钥空间通常会影响轻量级加密技术。EHO（大象群优化）通过调整迭代轮次来优化密钥空间。主要目标是使用所提出的 ITPKLEIN-EHO 方法对医疗保健中的 EEG（脑电图）进行加密，并将其发送给最终用户。该建议的技术在各种 EEG 数据集上使用 MATLAB 进行了测试。所提出的 IRPKLEIN-EHO 技术的仿真在 MSE、PSNR、SSIM、PRD 和加密/解密时间方面与其他现有技术进行了评估。

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