QuripfeNet: Quantum-Resistant IPFE-based Neural Network

KyungHyun Han, Wai-Kong Lee, Angshuman Karmakar, Myung-Kyu Yi, Seong Oun Hwang

Abstract: In order to protect the sensitive information in many applications involving neural networks, several privacypreserving 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 quantumresistant 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 latticebased 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 polynomialbased 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.