We present an approach to outsourcing of training neural networks while preserving data confidentiality from malicious parties. We use fully homomorphic encryption to build a unified training approach that works on encrypted data and learns quantized neural network models. The data can be horizontally or vertically split between multiple parties, enabling collaboration on confidential data. We train logistic regression and multi-layer perceptrons on several datasets.

翻译：我们提出了一种在保护数据免受恶意方泄密的前提下外包神经网络训练的方法。采用全同态加密构建统一的训练方案，该方案可直接处理加密数据并学习量化神经网络模型。数据支持在多方之间进行水平或垂直分割，从而实现对机密数据的协作处理。我们在多个数据集上训练了逻辑回归和多层感知器模型。