论文标题
使用流模型保护数据共享
Secure Data Sharing With Flow Model
论文作者
论文摘要
在经典的多方计算设置中，多方共同计算一个函数，而无需透露自己的输入数据。我们考虑了此问题的一个变体，其中可以共享输入数据以用于机器学习培训目的，但是数据也已加密，以便其他方无法恢复它们。我们提出了一种使用流模型的基于旋转的方法，并理论上证明了其安全性。我们在不同的情况下演示了我们方法的有效性，包括受监督的安全模型培训和无监督的生成模型培训。我们的代码可在https://github.com/ duchenzhuang/flowencrypt上找到。
In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data. We consider a variant of this problem, where the input data can be shared for machine learning training purposes, but the data are also encrypted so that they cannot be recovered by other parties. We present a rotation based method using flow model, and theoretically justified its security. We demonstrate the effectiveness of our method in different scenarios, including supervised secure model training, and unsupervised generative model training. Our code is available at https://github.com/ duchenzhuang/flowencrypt.
