论文标题
有效的私人机器学习通过可区分的随机转换
Efficient Private Machine Learning by Differentiable Random Transformations
论文作者
论文摘要
随着对隐私保护的需求越来越多，近年来提出了许多隐私机器学习系统。但是，由于同质加密和安全的多方计算（MPC）方法，大多数人无法将它们的慢训练和推理速度放在生产中。为了避免这种情况，我提出了一个隐私定义，该定义适用于机器学习任务中的大量数据。基于此，我表明，线性转换和随机置换等随机转换可以很好地保护隐私。我将随机转换和算术共享合并在一起，我设计了一个以高效率和低计算成本的私人机器学习框架。
With the increasing demands for privacy protection, many privacy-preserving machine learning systems were proposed in recent years. However, most of them cannot be put into production due to their slow training and inference speed caused by the heavy cost of homomorphic encryption and secure multiparty computation(MPC) methods. To circumvent this, I proposed a privacy definition which is suitable for large amount of data in machine learning tasks. Based on that, I showed that random transformations like linear transformation and random permutation can well protect privacy. Merging random transformations and arithmetic sharing together, I designed a framework for private machine learning with high efficiency and low computation cost.
