Multiparty Homomorphic Machine Learning with Data Security and Model Preservation

Abstract

With the widespread application of machine learning (ML), data security has been a serious issue. To eliminate the conflict between data privacy and computability, homomorphism is extensively researched due to its capacity of performing operations over ciphertexts. Considering that the data provided by a single party are not always adequate to derive a competent model via machine learning, we proposed a privacy-preserving training method for the neural network over multiple data providers. Moreover, taking the trainer's intellectual property into account, our scheme also achieved the goal of model parameter protection. Thanks to the hardness of the conjugate search problem (CSP) and discrete logarithm problem (DLP), the confidentiality of training data and system model can be reduced to well-studied security assumptions. In terms of efficiency, since all messages are coded as low-dimensional matrices, the expansion rates with regard to storage and computation overheads are linear compared to plaintext implementation without accuracy loss. In reality, our method can be transplanted to any machine learning system involving multiple parties due to its capacity of fully homomorphic computation.