提高医疗保健数据安全性,使用RFE和CRHSM用于大数据
C Sreedhar1, K Mahesh Babu2, Suresh Kallam3
1Department of CSE, G. Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India.
Computers in biology and medicine
|March 28, 2025
概括
这项研究引入了一种新的基于连贯随机哈希的安全模型 (CRHSM),以提高医疗保健云安全性. 该模型改进了数据保护和用户身份验证,解决了医疗大数据系统中的效率和复杂性问题.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 医疗保健信息学 医疗保健信息学
背景情况:
- 医疗云中的医疗大数据安全性至关重要,但面临着复杂性和低效率等挑战.
- 现有的加密方法经常因高的操作需求和存储开销而扎.
研究的目的:
- 开发一个先进的数据安全框架,以提高医疗保健系统的可靠性.
- 引入一种基于Cohesive Random-Hash的新型安全模型 (CRHSM),用于强大的用户身份验证和医疗记录保护.
主要方法:
- 拟议的基于Cohesive Random-Hash的安全模型 (CRHSM) 集成了松鼠搜索优化 (SSO) 来在注册过程中生成随机数字.
- 它采用改革派Feistel加密 (RFE) 机制,在云存储之前加密医疗记录.
- 该模型在医院环境中管理系统初始化,注册,登录,身份验证,加密和解密.
主要成果:
- 在医疗云环境中,CRHSM模型有效地保护用户身份验证和医疗记录.
- 性能评估证明了与现有安全方法相比,该模型的有效性.
- 该框架解决了以前方法的局限性,提供了更高的效率和更少的复杂性.
结论:
- 新的CRHSM框架为医疗云中的医疗大数据提供了可靠和高效的解决方案.
- 它通过保护敏感的患者信息来提高医疗保健系统的整体安全状况.
- 拟议的模型代表了保护医疗数据免受未经授权的访问和攻击的重大进步.
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