Practical multi-party quantum homomorphic encryption

Lv, Chen; Chen, Lingli; Li, Qin

doi:10.1016/j.tcs.2023.114067

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…The privacy protection of quantum machine learning is an emergent field and there is a lot of research about it. Most of the existing literature protects privacy by means of quantum homomorphic encryption [15][16][17][18][19][20][21][22], quantum differential privacy [23,24] and quantum secure multi-party computing [25][26][27]. Each method achieves privacy protection in different ways and presents different levels of security and availability.…”

Section: Introductionmentioning

confidence: 99%

Quantum neural network with privacy protection of input data and training parameters

Fang,

Chang

2024

Phys. Scr.

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It takes a lot of computational resources to train a machine learning model. So does quantum machine learning. In the NISQ (Noisy Intermediate Scale Quantum) era, there is a need for users who cannot afford quantum computers to utilize quantum servers to complete quantum machine learning with protection privacy of data and model parameters. In this paper, novel homomorphic encryption methods and circuit design with hidden parameter for R z , R x and R y gate are putting forward. Based on which, we further propose quantum neural network (QNN) with privacy protection of input data and training parameters, where hiding model parameters has hardly mentioned in existing literature. To verify the validity of the proposed scheme, we conducted experiments on MNIST and Iris datasets, and obtained losses and accuracy that are almost identical to the original algorithms. Experiments show that our scheme can protect privacy well in the training and have no effect on accuracy of the algorithm. By comparison, the proposed scheme is more secure than most existing literature and requires less extra complexity O(4n).

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