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基于CAE模型的安全脱复制方法
Chunbo Wang1, Guoying Zhang1, Hui Qi1
1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
Scientific reports
|July 9, 2025
概括
本研究介绍了一种使用Convolutional Autoencoder (CAE) 模型的新型安全云数据脱复制方法. 与现有技术相比,这种方法显著提高了效率,并减少了计算开销.
科学领域:
- 计算机科学 计算机科学
- 数据存储数据存储数据存储
- 云计算 云计算 云计算
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 云存储提供了方便,但遭受重复数据,增加存储负担和隐私风险.
- 现有的安全脱复制方法,如随机消息锁定加密 (R-MLE),由于双线映射,面临着计算挑战.
研究的目的:
- 开发一种安全高效的云数据脱复制方法.
- 为了减少与传统安全脱复制技术相关的计算开销.
- 在云环境中提高数据隐私和存储效率.
主要方法:
- 提出了一种使用Autoencoder模型生成摘要标签的安全除重复方法.
- 引入了一个卷积自编码器 (CAE) 模型,以进一步减少参数,计算和存储开销.
- 在CAE中使用卷积和聚合操作,以提高效率和减轻过度拟合.
主要成果:
- 拟议的自编码器和基于CAE的方法显著提高了除重复的效率.
- 通过CAE方法观察到模型存储需求减少.
- 实验表明数据分布更加均,表明存储利用率更好.
结论:
- 基于CAE的安全脱复制方法比现有技术提供了更高的性能.
- 这种方法有效地解决了与重复云数据相关的存储负担和隐私问题.
- 该方法为高效和安全的云数据管理提供了有前途的解决方案.
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