论文标题
化学：有效的安全汇总，并在联合机器学习系统中使用缓存的同态加密
CHEM: Efficient Secure Aggregation with Cached Homomorphic Encryption in Federated Machine Learning Systems
论文作者
论文摘要
尽管可以将同态加密纳入神经网络层中，以确保机器学习任务，例如对加密数据样本的机密推断和在联合学习中加密的本地模型，但计算开销一直是致命弱点。本文提出了一种缓存协议，即化学，以便可以从缓存的辐射池构建张量的密文，而不是进行昂贵的加密操作。从理论的角度来看，我们证明了化学在语义上是安全的，并且可以在实际假设下通过直接分析进行参数化。三个流行的公共数据集的实验结果表明，采用化学仅会产生次秒的开销，但分别用于编码机密推断中的输入数据样本和67％-87％的加密成本为89％-87％-87％-87％，分别为联合学习中的本地模型编码本地模型。
Although homomorphic encryption can be incorporated into neural network layers for securing machine learning tasks, such as confidential inference over encrypted data samples and encrypted local models in federated learning, the computational overhead has been an Achilles heel. This paper proposes a caching protocol, namely CHEM, such that tensor ciphertexts can be constructed from a pool of cached radixes rather than carrying out expensive encryption operations. From a theoretical perspective, we demonstrate that CHEM is semantically secure and can be parameterized with straightforward analysis under practical assumptions. Experimental results on three popular public data sets show that adopting CHEM only incurs sub-second overhead and yet reduces the encryption cost by 48%--89% for encoding input data samples in confidential inference and 67%--87% for encoding local models in federated learning, respectively.
