基于多层模型的增强安全存储和数据隐私管理系统
1School of General Education, Sichuan Vocational and Technical College, Suining, 629000, Sichuan, China. tang.ting1970@outlook.com.
Scientific reports
|September 2, 2025
概括
本研究介绍了一种多层安全云存储模型 (MLSCSM),用于保护大数据系统中的敏感人员数据. 这种模式提高了云环境的安全性和效率.
科学领域:
- 计算机科学
- 数据安全性
- 云计算
背景情况:
- 管理和保护大规模敏感数据,特别是人员记录,是云环境中的一个重大挑战.
- 大数据系统的日益复杂和规模需要先进的安全解决方案.
研究的目的:
- 提出一个新的多层安全云存储模型 (MLSCSM),用于云环境中的大规模人员数据.
- 整合加密和统计方法来保护隐私和安全的数据存储.
主要方法:
- MLSCSM结合了ChaCha20加密,双阶段数据分区 (DSDP),k-匿名化,SHA-512哈希和基于Cauchy矩阵的分散.
- 数据块根据各种因素安全编码,掩盖和分布在多个云平台上.
- 该模型包含审计日志,负载平衡和实时资源评估.
主要成果:
- 拟议的模型实现了250 ms的编码时间 (区块大小75),23%的CPU使用量为256 MB的数据,以及14 ms的低延迟.
- 显示高吞吐量达139毫秒,超过了RDFA,SDPMC和P&XE等基线模型.
- 在Hadoop集群上使用MIMIC-III数据集的验证证实了该系统的有效性.
结论:
- MLSCSM为基于云的大数据存储应用提供了卓越的安全性,效率和可扩展性.
- 结合加密和统计技术为保护隐私的数据管理提供了强大的解决方案.
- 该模型针对分布式云计算环境 (CCE) 进行了优化.
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