一个基于四重目标的云隐私保护模型与建议的关联规则隐藏和深度学习协助了最佳的密钥生成
1Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, India.
概括
本研究介绍了使用数据清洁和最佳密钥生成的云隐私模型. 拟议的方法增强了对未经授权访问的数据安全性,优于现有的解决方案.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 现有的云隐私解决方案在敏感数据保护方面扎.
- 需要一个强大的模型来确保云环境中的数据保密性.
研究的目的:
- 为云环境提出一种新的隐私保护模型.
- 加强敏感数据的安全性,防止未经授权的访问.
主要方法:
- 使用基于增强动态项目集计数 (ADIC) 的关联规则挖矿来识别敏感数据.
- 使用LSTM和混合优化 (MUAOA) 的深度学习方法生成最佳调整键.
- 使用生成的调整密钥和数据恢复进行数据清理.
主要成果:
- 拟议的LSTM+MUAOA模型证明了卓越的隐私保护.
- 在比较评估中,与现有模型相比,实现了更高的隐私值 (5.21).
结论:
- 开发的隐私保护模型有效地保护云中的敏感数据.
- 集成ADIC,LSTM和MUAOA为先进的数据安全提供了一个有前途的方法.
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