对于有未知但有边界噪声的人工神经网络的安全状态估计:一个同态加密方案
IEEE transactions on neural networks and learning systems
|April 24, 2024
概括
本研究介绍了使用同型加密 (HES) 的人工神经网络 (ANN) 的安全状态估计方法. 它可以在没有数据解密的有限带宽网络上进行安全估计,确保数据完整性.
科学领域:
- 控制系统工程 控制系统工程
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 对于有噪音数据和有限通信的系统,安全状态估计至关重要.
- 人工神经网络 (ANN) 越来越多地使用,但需要安全的数据处理.
- 开放的,带宽有限的网络在传输敏感测量数据方面带来了挑战.
研究的目的:
- 开发一个安全的状态估计算法,用于在未知但有界噪声下ANN.
- 通过使用新型加密技术在带宽有限的网络上传输数据时确保数据安全.
- 能够直接从加密数据中进行状态估计,而无需进行解密.
主要方法:
- 一个新的同型加密方案 (HES),结合编码解码机制 (EDM) 和Paillier加密.
- 在加密数据上运行的安全集合成员状态估计算法的开发.
- 使用优化和拉格朗奇乘法推导安全状态估计器收益.
主要成果:
- 在噪声和HES约束下确定了圆形集存在的足够条件.
- 拟议的安全状态估计算法有效地从加密数据计算估计.
- 该方法确保了整个估计过程中的数据安全性.
结论:
- 开发的安全状态估计方法对杂的,带宽有限的环境中的ANN有效.
- 同型加密方案在传输和估计过程中提供了强大的数据保护.
- 这项工作促进了网络控制系统中安全可靠的状态估计.
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