QuripfeNet: Quantum-Resistant IPFE-Based Neural Network

초록

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. © 2013 IEEE.

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