QuripfeNet: Quantum-Resistant IPFE-Based Neural Network

Abstract

In order to protect the sensitive information in many applications involving neural networks, several privacy-preserving neural networks that operate on encrypted data have been developed. Unfortunately, existing encryption-based privacy-preserving neural networks are mainly built on classical cryptography primitives, which are not secure from the threat of quantum computing. In this paper, we propose the first quantum-resistant solution to protect neural network inferences based on an inner-product functional encryption scheme. The selected state-of-the-art functional encryption scheme based on lattice-based cryptography works with integer-type inputs, which is not directly compatible with neural network computations that operate in the floating point domain. We propose a polynomial-based secure convolution layer to allow a neural network to resolve this problem, along with a technique that reduces memory consumption. The proposed solution, named QuripfeNet, was applied in LeNet-5 and evaluated using the MNIST dataset. In a single-threaded implementation (CPU), QuripfeNet took 107.4 seconds for an inference to classify one image, achieving accuracy of 97.85%, which is very close to the unencrypted version. Additionally, the GPU-optimized QuripfeNet took 25.9 seconds to complete the same task, which is improved by 4.15× compared to the CPU version.