利用 A-BRSA 和 Salp -Ant Lion 优化算法实现安全的云医疗数据共享

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE CAAI Transactions on Intelligence Technology Pub Date : 2024-05-17 DOI:10.1049/cit2.12305
Adel Binbusayyis, Abed Alanazi, Shtwai Alsubai, Areej Alasiry, M. Marzougui, Abdullah Alqahtani, Mohemmed Sha, Muhammad Aslam
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引用次数: 0

摘要

医疗服务提供者、研究人员和患者之间共享医疗数据对于高效的医疗服务至关重要。云辅助智能医疗保健(S-healthcare)系统使有效存储电子病历变得更加容易。然而,如果加密密钥被泄露,用于保护这些数据安全的传统加密算法就很容易受到攻击,从而构成安全威胁。本文提出了一种基于云的安全医疗数据共享系统,它采用了一种名为 A-BRSA 的混合加密模型,结合了基于属性的加密(ABE)和 B-RSA 加密。该系统利用 Salp-Ant Lion 优化算法来优化密钥选择。加密数据存储在云中并传输给接收方,接收方使用基于 A-BRSA 的解密方法对数据进行解密。研究测量了周转时间、加密时间、解密时间和恢复效率,以评估系统的性能。研究结果表明,A-BRSA 模型能有效确保基于云的 s-healthcare 系统中医疗数据的安全共享。
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A secured cloud‐medical data sharing with A‐BRSA and Salp ‐Ant Lion Optimisation Algorithm
Sharing medical data among healthcare providers, researchers, and patients is crucial for efficient healthcare services. Cloud‐assisted smart healthcare (s‐healthcare) systems have made it easier to store EHRs effectively. However, the traditional encryption algorithms used to secure this data can be vulnerable to attacks if the encryption key is compromised, posing a security threat. A secured cloud‐based medical data‐sharing system is proposed using a hybrid encryption model called A‐BRSA, which combines attribute‐based encryption (ABE) and B‐RSA encryption. The system utilises the Salp‐Ant Lion Optimisation Algorithm for optimal key selection. The encrypted data is stored in the cloud and transmitted to the recipient, where it is decrypted using A‐BRSA‐based decryption. The study measures turnaround time, encryption time, decryption time, and restoration efficiency to evaluate the system's performance. The results demonstrate the effectiveness of the A‐BRSA model in ensuring secure medical data sharing in cloud‐based s‐healthcare systems.
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
期刊最新文献
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