A Study of Privacy-Preserving Neural Network Prediction Based on Replicated Secret Sharing.

Neural networks have a wide range of promise for image prediction, but in the current setting of neural networks as a service, the data privacy of the parties involved in prediction raises concerns. In this paper, we design and implement a privacy-preserving neural network prediction model in the three-party secure computation framework over secret sharing of private data. Secret sharing allows the original data to be split, with each share held by a different party. The parties cannot know the shares owned by the remaining collaborators, and thus the original data can be kept secure. The three parties refer to the client, the service provider and the third server that assist in the computation, which is different from the previous work. Thus, under the definition of semi-honest and malicious security, we design new computation protocols for the building blocks of the neural network based on replicated secret sharing. Experimenting with MNIST dataset on different neural network architectures, our scheme improves 1.3×/1.5× and 7.4×/47.6× in terms of computation time as well as communication cost compared to the Falcon framework under the semi-honest/malicious security, respectively. [ABSTRACT FROM AUTHOR]