边缘协同的轻量级隐私保护分类框架

doi:10.11959/j.issn.1000-436x.2022004

Abstract

Abstract:

Aiming at the problems of data leakage of perceptual image and computational inefficiency of privacy-preserving classification framework in edge-side computing environment, a lightweight and privacy-preserving classification framework (PPCF) was proposed to supports encryption feature extraction and classification, and achieve the goal of data transmission and computing security under the collaborative classification process of edge nodes.Firstly, a series of secure computing protocols were designed based on additive secret sharing.Furthermore, two non-collusive edge servers were used to perform secure convolution, secure batch normalization, secure activation, secure pooling and other deep neural network computing layers to realize PPCF.Theoretical and security analysis indicate that PPCF has excellent accuracy and proved to be security.Actual performance evaluation show that PPCF can achieve the same classification accuracy as plaintext environment.At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPCF has obvious advantages in terms of computational cost and communication overhead.

Key words: edge-collaborative, privacy-preserving object classification, additive secret sharing, deep neural network, se-cure computing protocol

CLC Number:

TN309

Jinbo XIONG, Yongjie ZHOU, Renwan BI, Liang WAN, Youliang TIAN. Towards edge-collaborative, lightweight and privacy-preserving classification framework[J]. Journal on Communications, 2022, 43(1): 127-137.

Figures/Tables 9

安全计算协议	通信轮数/轮	通信开销
SConv	1	$2 N ‖ N ‖$
SBN	7	$13 N ‖ N ‖$
SRU6	6	$8 N ‖ N ‖$
SF	1	$2 N ‖ N ‖$

References 26

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协议	ResNet^[21]	FALCON^[20]	PPCF
SConv	O (N)	O (N)	O (N)
SFC	O (N)	O (N)	O (N)
SGAP	O (1)	—	O (1)
SBN	O (N)	O (3N)	O (N)
SRU6	O (N)	O(2Nlogl)	O (N)

模型	计算开销/s	通信开销/MB	分类精度
ResNet^[21]	1.06	—	99.83%
PPCF	5.10	145.3	99.83%

协议	文献[17]	文献[19]	文献[20]	PPCF
SConv	√	√	√	√
SGAP	—	—	—	√
SFC	√	√	√	√
SBN	—	—	√	√
SRU6	—	—	—	√
保护训练参数	—	√	√	√

Towards edge-collaborative, lightweight and privacy-preserving classification framework

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