Preserving Multi-party Machine Learning with Homomorphic Encryption

Privacy preserving multi-party machine learning approaches enable multiple parties to train a machine learning model from aggregate data while ensuring the privacy of their individual datasets is preserved. In this paper, we propose a privacy preserving multi-party machine learning approach based on homomorphic encryption where the machine learning algorithm of choice is deep neural networks. We develop theoretical foundation for implementing deep neural networks over encrypted data and utilize it in developing efficient and practical algorithms in encrypted domain.