Non-interactive and privacy-preserving neural network learning using functional encryption

Abstract

Fast and reliable modeling of distributed data with sensitive information is a major goal of privacy-preserving machine learning (PPML). Artificial Neural Networks (ANNs) are powerful and scalable, making them suitable to settle large-scale and highly complex machine learning tasks. Since a large number of neurons and various non-linear functions are embedded in ANNs, it is still a huge challenge to train an ANNs model in the encrypted-domain. Majority of existing approaches for PPML require multiple communications among data owners and cloud servers, leading to a substantial overhead of computation and data transmission costs, and are restricted to approximation models by polynomials or piecewise linear functions. Here, to facilitate the integration of any training data from any data owner without violating privacy constraints, we introduce a non-interactive and lossless ANNs training approach that unites mask matrix, function encryption for inner-product and coordination while maintaining confidentiality with the help of Internet Service Providers (ISPs), thereby going beyond those schemes with interactive fashion and using approximation substitutions. To illustrate the feasibility and efficiency of using our method to develop ANNs classifiers using distributed data, we choose two well-known MNIST datasets with a large amount of image data with high dimensionality in PPML field. We show the protocol is feasible to use in practice while achieving high accuracy. Furthermore, the evaluation reveals that the computational efficiency is improved at least 25 times compared with the state-of-the-art privacy-preserving ANNs approach.