边缘协同的轻量级隐私保护分类框架

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

通信学报 ›› 2022, Vol. 43 ›› Issue (1): 127-137.doi: 10.11959/j.issn.1000-436x.2022004

边缘协同的轻量级隐私保护分类框架

熊金波¹^,²^,³, 周永洁¹^,⁴, 毕仁万², 万良¹, 田有亮¹^,³^,⁴

¹ 贵州大学计算机科学与技术学院，贵州贵阳 550025
² 福建师范大学计算机与网络空间安全学院，福建福州 350117
³ 贵州省公共大数据重点实验室，贵州贵阳 550025
⁴ 贵州大学密码学与数据安全研究所，贵州贵阳 550025

修回日期:2021-12-15 出版日期:2022-01-25 发布日期:2022-01-01
作者简介:熊金波（1981- ），男，湖南益阳人，博士，福建师范大学教授、博士生导师，主要研究方向为安全深度学习、移动群智感知、隐私保护技术等
周永洁（1996- ），女，贵州镇远人，贵州大学硕士生，主要研究方向为安全深度学习、隐私保护技术等
毕仁万（1996- ），男，湖南常德人，福建师范大学博士生，主要研究方向为安全深度学习、安全多方计算等
万良（1974- ），男，贵州铜仁人，博士，贵州大学教授、硕士生导师，主要研究方向为网络空间安全等
田有亮（1982- ），男，贵州六盘水人，博士，贵州大学教授、博士生导师，主要研究方向为算法博弈论、密码学与安全协议、大数据安全与隐私保护、区块链与电子货币等
基金资助:
国家自然科学基金资助项目(61872090);国家自然科学基金资助项目(61872088);国家自然科学基金资助项目(U1836205);国家自然科学基金资助项目(61772008);贵州省科技重大专项计划基金资助项目(20183001);福建省自然科学基金资助项目(2019J01276);贵州省科技计划基金资助项目([2019]1098);贵州省高层次创新型人才基金资助项目([2020]6008);贵阳市科技计划基金资助项目([2021]1-5)

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

Jinbo XIONG¹^,²^,³, Yongjie ZHOU¹^,⁴, Renwan BI², Liang WAN¹, Youliang TIAN¹^,³^,⁴

¹ College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
² College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China
³ State Key Laboratory of Public Big Date, Guiyang 550025, China
⁴ Institute of Cryptography ＆Data Security, Guizhou University, Guiyang 550025, China

Revised:2021-12-15 Online:2022-01-25 Published:2022-01-01
Supported by:
The National Natural Science Foundation of China(61872090);The National Natural Science Foundation of China(61872088);The National Natural Science Foundation of China(U1836205);The National Natural Science Foundation of China(61772008);Science and Technology Major Support Program of Guizhou Province(20183001);The Natural Science Foundation of Fujian Prov-ince(2019J01276);Science and Tech-nology Program of Guizhou Province([2019]1098);Project of High-level Innovative Talents of Guizhou Province([2020]6008);Science and Technology Program of Guiyang Province([2021]1-5)

摘要/Abstract

摘要：

针对边端计算环境下存在感知图像数据泄露与隐私保护分类框架计算低效的问题，提出一种边缘协同的轻量级隐私保护分类框架（PPCF），该框架支持加密特征提取和分类，在边缘节点协同分类过程中实现对数据传输和计算过程的隐私保护。首先，基于加性秘密共享技术设计一系列安全计算协议；在此基础上，两台非共谋的边缘服务器协同执行安全卷积、安全批量归一化、安全激活、安全池化等深度神经网络计算层以实现 PPCF。理论与安全性分析证明了PPCF的正确性和安全性，性能评估结果显示，PPCF可达到与明文环境等同的分类精度；与同态加密和多轮迭代计算方案相比，PPCF在计算开销和通信开销方面具有明显优势。

关键词: 边缘协同, 隐私保护目标分类, 加性秘密共享, 深度神经网络, 安全计算协议

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

中图分类号:

TN309

熊金波, 周永洁, 毕仁万, 万良, 田有亮. 边缘协同的轻量级隐私保护分类框架[J]. 通信学报, 2022, 43(1): 127-137.

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.

图/表 9

图1

图2

图3

图4

表1

表2

安全计算协议的通信复杂度"

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	$2 N ‖ N ‖$

表2

图6

表3

表4

参考文献 26

[1]	NGUYEN K X , RYU T , ZHANG J ,et al. CoConv:learning dynamic cooperative convolution for image recognition[C]// Proceedings of 2021 IEEE International Conference on Multimedia and Expo. Piscataway:IEEE Press, 2021: 1-6.
[2]	YANG Q , FU S , WANG H G ,et al. Machine-learning-enabled cooperative perception for connected autonomous vehicles:challenges and opportunities[J]. IEEE Network, 2021,35(3): 96-101.
[3]	GUO J D , CARRILLO D , TANG S H ,et al. CoFF:cooperative spatial feature fusion for 3-D object detection on autonomous vehicles[J]. IEEE Internet of Things Journal, 2021,8(14): 11078-11087.
[4]	XIONG J B , BI R W , TIAN Y L ,et al. Towards lightweight,privacy-preserving cooperative object classification for connected autonomous vehicles[J]. IEEE Internet of Things Journal, 3573,PP(99): 1.
[5]	LIN L , LIAO X F , JIN H ,et al. Computation offloading toward edge computing[J]. Proceedings of the IEEE, 2019,107(8): 1584-1607.
[6]	YANG S Z , GONG Z , YE K ,et al. EdgeCNN:convolutional neural network classification model with small inputs for edge computing[J]. arXiv Preprint,arXiv:1909.13522, 2019.
[7]	WANG Y B , HONG K J , ZOU J ,et al. A CNN-based visual sorting system with cloud-edge computing for flexible manufacturing systems[J]. IEEE Transactions on Industrial Informatics, 2020,16(7): 4726-4735.
[8]	WONG A , LIN Z Q , CHWYL B . AttoNets:compact and efficient deep neural networks for the edge via human-machine collaborative design[C]// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway:IEEE Press, 2019: 684-693.
[9]	HESAMIFARD E , TAKABI H , GHASEMI M ,et al. Privacy-preserving machine learning as a service[J]. Proceedings on Privacy Enhancing Technologies, 2018,2018(3): 123-142.
[10]	GILAD-BACHRACH R , DOWLIN N , LAINE K ,et al. CryptoNets:applying neural networks to encrypted data with high throughput and accuracy[C]// Proceedings of the 33rd International Conference on Machine Learning. Saarland:DBLP, 2016: 201-210.
[11]	CHOU E , BEAL J , LEVY D ,et al. Faster CryptoNets:leveraging sparsity for real-world encrypted inference[J]. arXiv Preprint,arXiv:1811.09953, 2018.
[12]	AL-BADAWI A , JIN C , LIN J ,et al. Towards the AlexNet moment for homomorphic encryption:HCNN,the first homomorphic CNN on encrypted data with GPUs[J]. IEEE Transactions on Emerging Topics in Computing, 2021,9(3): 1330-1343.
[13]	ZHANG Q C , YANG L T , CHEN Z K . Privacy preserving deep computation model on cloud for big data feature learning[J]. IEEE Transactions on Computers, 2016,65(5): 1351-1362.
[14]	ROUHANI B D , RIAZI M S , KOUSHANFAR F . DeepSecure:scalable provably-secure deep learning[C]// Proceedings of 2018 55th ACM/ESDA/IEEE Design Automation Conference. Piscataway:IEEE Press, 2018: 1-6.
[15]	LIU J , JUUTI M , LU Y ,et al. Oblivious neural network predictions via MiniONN transformations[C]// Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York:ACM Press, 2017: 619-631.
[16]	RIAZI M S , WEINERT C , TKACHENKO O ,et al. Chameleon:a hybrid secure computation framework for machine learning applications[C]// Proceedings of the 2018 on Asia Conference on Computer and Communications Security. New York:ACM Press, 2018: 707-721.
[17]	HUANG K , LIU X M , FU S J ,et al. A lightweight privacy-preserving CNN feature extraction framework for mobile sensing[J]. IEEE Transactions on Dependable and Secure Computing, 2021,18(3): 1441-1455.
[18]	熊金波, 毕仁万, 陈前昕 ,等. 边缘协作的轻量级安全区域建议网络[J]. 通信学报, 2020,41(10): 188-201.
	XIONG J B , BI R W , CHEN Q X ,et al. Towards edge-collaborative,lightweight and secure region proposal network[J]. Journal on Communications, 2020,41(10): 188-201.
[19]	WAGH S , GUPTA D , CHANDRAN N . SecureNN:3-party secure computation for neural network training[J]. Proceedings on Privacy Enhancing Technologies, 2019,2019(3): 26-49.
[20]	WAGH S , TOPLE S , BENHAMOUDA F ,et al. Falcon:honest-majority maliciously secure framework for private deep learning[J]. Proceedings on Privacy Enhancing Technologies, 2021,2021(1): 188-208.
[21]	HE K M , ZHANG X Y , REN S Q ,et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 770-778.
[22]	SHAMIR A . How to share a secret[J]. Communications of the ACM, 1979,22(11): 612-613.
[23]	毕仁万, 陈前昕, 熊金波 ,等. 面向深度神经网络的安全计算协议设计方法[J]. 网络与信息安全学报, 2020,6(4): 130-139.
	BI R W , CHEN Q X , XIONG J B ,et al. Design method of secure computing protocol for deep neural network[J]. Chinese Journal of Network and Information Security, 2020,6(4): 130-139.
[24]	LIU Y , MA Z , LIU X M ,et al. Privacy-preserving object detection for medical images with faster R-CNN[J]. IEEE Transactions on Information Forensics and Security, 2022,17: 69-84.
[25]	BOGDANOV D , NIITSOO M , TOFT T ,et al. High-performance secure multi-party computation for data mining applications[J]. International Journal of Information Security, 2012,11(6): 403-418.
[26]	XIONG J B , BI R W , ZHAO M F ,et al. Edge-assisted privacy-preserving raw data sharing framework for connected autonomous vehicles[J]. IEEE Wireless Communications, 2020,27(3): 24-30.

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	—	√	√	√

边缘协同的轻量级隐私保护分类框架

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

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