Image Classification Method Based on Secure Multiparty Computation

doi:10.3969/j.issn.1671-1122.2023.11.004

Abstract

Abstract:

This paper focused on researching image classification methods based on secure multiparty computation. To solve the problem that the PaddleFL method based on the ABY³ protocol cannot support some network encryption operations in complex models, this paper proposed an encryption method for dimension transformation and compression operations based on the repeated secret sharing of the ABY³ protocol. To solve the model collapse problem of the CrypTen method based on the Beaver protocol during ciphertext training, this paper proposed a detection method based on a flag to discard abnormal values to avoid wrap-around errors during training, and introduce a ciphertext calculation method based on threshold restriction for the softmax function to eliminate approximation calculation errors, meeting the requirement of ciphertext calculation of a larger numerical range. Experimental results on public datasets show that the proposed method can effectively protect user data privacy while ensuring model accuracy.

Key words: image classification, privacy-preserving machine learning, secure multiparty computation, PaddleFL, CrypTen

CLC Number:

TP309

SUN Yongqi, SONG Zewen, ZHU Weiguo, ZHAO Sicong. Image Classification Method Based on Secure Multiparty Computation[J]. Netinfo Security, 2023, 23(11): 27-37.

Figures/Tables 11

References 27

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网络层	尺寸	参数（步长，填充）
Convolution+ReLU	16×3×3	（1, 1）
MaxPooling	16×2×2	(2, 0)
Convolution+ReLU	32×3×3	（1, 1）
MaxPooling	32×2×2	(2, 0)
Convolution+ReLU	64×3×3	（1, 1）
Convolution+ReLU	64×2×2	(1, 1)
FullyConnected	50	—
MaxPooling	64×2×2	(2, 0)
Convolution+ReLU+BtchNorm	128×3×3	（1, 1）
Convolution+ReLU+BtchNorm	128×3×3	(1, 1)
FullyConnected	54	—
MaxPooling	128×2×2	(2, 0)
Convolution	128×2×2	(1, 0)
Transpose	—	perm=(1, 2, 0)
Squeeze	—	axes=0
FullyConnected +Softmax	38	—

轮次/轮	发生错误数/次	平均损失绝对值
5	1	4202790
21	2	36029366
22	1	6622046
23	2	3293641
25	2	26864721
26	3	14861348
27	2	9939613
29	2	31685981
30	1	4444139

数值范围	CrypTen/s	本文方法/s
±0~1	0.000348	0.000349
±1~10	0.000427	0.000426
±10~100	0.000107	0.000123
±100~1000	—	0.000072
±1000~10000	—	0.000068

数值范围	CrypTen/s	本文方法/s
±0~1	0.5056	0.5286
±1~10	0.5058	0.5261
±10~100	0.5051	0.5217
±100~1000	—	0.5291
±1000~10000	—	0.5266

框架	数据集	准确率	推理时间/s
PaddlePaddle	MNIST	98.47%	2.05×10^-5
	Fashion-MNIST	87.90%	2.03×10^-5
	CIFAR-10	59.23%	2.37×10^-5
PaddleFL	MNIST	98.47%	0.000955
	Fashion-MNIST	87.90%	0.000845
	CIFAR-10	59.23%	0.001182
PyTorch	MNIST	98.48%	4.03×10^-6
	Fashion-MNIST	87.47%	3.94×10^-6
	CIFAR-10	58.24%	4.21×10^-6
CrypTen	MNIST	98.48%	0.0159
	Fashion-MNIST	87.47%	0.0159
	CIFAR-10	58.24%	0.0211