Abstract:
One main objective of this paper is to show how to adapt the well-known, lattice-based NTRU post-quantum encryption to the confidential inference in decision trees. Anoth...Show MoreMetadata
Abstract:
One main objective of this paper is to show how to adapt the well-known, lattice-based NTRU post-quantum encryption to the confidential inference in decision trees. Another objective is to describe a resource-efficient FPGA implementation of the adapted NTRU. The typical use case of such encryption is that of two parties where one party has proprietary ownership of the decision tree model while the other party has proprietary ownership of the data. Confidential inference in decision trees can be insured using order-preserving cryptography, which has much weaker requirements and is therefore easier to implement than fully homomorphic cryptography. Post-quantum NTRU is not order-preserving, but interestingly, it can be modified to obey the order-preserving property. We call the resulting cipher OP-NTRU. Lossless compression can be applied to the ciphertext produced by OP-NTRU to facilitate its hardware acceleration. OP-NTRU has been implemented on an FPGA with the HDL code automatically compiled from the machine learning framework. Confidential inference experiments report more than 96% compression without degrading inference accuracy in FPGA for the MNIST dataset.
Published in: 2023 IFIP/IEEE 31st International Conference on Very Large Scale Integration (VLSI-SoC)
Date of Conference: 16-18 October 2023
Date Added to IEEE Xplore: 22 November 2023
ISBN Information: