边缘协作的轻量级安全区域建议网络

doi:10.11959/j.issn.1000-436x.2020186

通信学报 ›› 2020, Vol. 41 ›› Issue (10): 188-201.doi: 10.11959/j.issn.1000-436x.2020186

边缘协作的轻量级安全区域建议网络

熊金波^1,²,毕仁万^1,²,陈前昕^1,²,刘西蒙^3,⁴()

¹ 福建师范大学数学与信息学院，福建福州 350117
² 福建师范大学福建省网络安全与密码技术重点实验室，福建福州 350007
³ 福州大学数学与计算机科学学院，福建福州 350108
⁴ 福州大学网络系统信息安全福建省高校重点实验室，福建福州 350108

修回日期:2020-07-20 出版日期:2020-10-25 发布日期:2020-11-05
作者简介:熊金波（1981- ），男，湖南益阳人，博士，福建师范大学教授，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|毕仁万（1996- ），男，湖南常德人，福建师范大学硕士生，主要研究方向为安全深度学习、安全多方计算等|陈前昕（1996- ），男，福建泉州人，福建师范大学硕士生，主要研究方向为安全深度学习、隐私保护技术等|刘西蒙（1988- ），男，陕西西安人，博士，福州大学教授，主要研究方向为云安全、应用密码学、大数据安全等
基金资助:
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(U1905211);国家自然科学基金资助项目(U1804263);国家自然科学基金资助项目(61702105);国家自然科学基金资助项目(61872090);福建省自然科学基金资助项目(2019J01276);贵州省公共大数据重点实验室开放课题基金资助项目(2019BDKFJJ004);陕西省网络与系统安全重点实验室开放课题基金资助项目(NSSOF1900104);广东省数据安全与隐私保护重点实验室开放课题基金资助项目(2017B03031004-12)

Towards edge-collaborative,lightweight and secure region proposal network

Jinbo XIONG^1,²,Renwan BI^1,²,Qianxin CHEN^1,²,Ximeng LIU^3,⁴()

¹ College of Mathematics and Informatics,Fujian Normal University,Fuzhou 350117,China
² Fujian Provincial Key Laboratory of Network Security and Cryptology,Fujian Normal University,Fuzhou 350007,China
³ College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108,China
⁴ Fujian Provincial Key Laboratory of Network System Information Security,Fuzhou University,Fuzhou 350108,China

Revised:2020-07-20 Online:2020-10-25 Published:2020-11-05
Supported by:
The National Natural Science Foundation of China(61872088);The National Natural Science Foundation of China(U1905211);The National Natural Science Foundation of China(U1804263);The National Natural Science Foundation of China(61702105);The National Natural Science Foundation of China(61872090);The Natural Science Foundation of Fujian Proince(2019J01276);The Guizhou Provincial Key Laboratory of Public Big Data Research Fund(2019BDKFJJ004);The Opening Project of Shaanxi Key Laboratory of Network and System Security(NSSOF1900104);The Opening Project of Guangdong Provincial Key Laboratory of Data Security and Privacy Protection(2017B03031004-12)

摘要/Abstract

摘要：

针对边缘环境下的图像隐私泄露和计算效率问题，提出一种边缘协作的轻量级安全区域建议网络（SecRPN）。基于加性秘密共享方案设计一系列安全计算协议，由2台非共谋边缘服务器协作执行安全特征处理、安全锚变换、安全边界框修正、安全非极大值抑制等计算模块。理论分析证明了SecRPN的正确性和安全性，实际性能评估表明，计算和通信开销均远优于现有工作。

关键词: 边缘协作, 区域建议网络, 目标检测, 加性秘密共享, 安全计算协议

Abstract:

Aiming at the problem of image privacy leakage and computing efficiency in edge environment,a lightweight and secure region proposal network (SecRPN) was proposed.A series of secure computing protocols were designed based on the additive secret sharing scheme.Two non-collusive edge servers cooperate to perform calculation modules such as secure feature processing,secure anchor transformation,secure bounding-box correction,and secure non-maximum suppression.Theoretical analysis guarantees the correctness and security of SecRPN.The actual performance evaluation shows that SecRPN is outstanding in the computational cost and communication overhead compared with the existing works.

Key words: edge-collaborative, region proposal network, object detection, additive secret sharing, secure computing protocol

中图分类号:

TP309.2

熊金波,毕仁万,陈前昕,刘西蒙. 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.

Jinbo XIONG,Renwan BI,Qianxin CHEN,Ximeng LIU. Towards edge-collaborative,lightweight and secure region proposal network[J]. Journal on Communications, 2020, 41(10): 188-201.

图/表 9

图1

图2

图3

表1

安全计算协议的计算复杂度"

协议	RPN^[17]	SecRCNN^[25]	SecRPN
SRU	$O (N \log N)$	$O (N L)$	$O (N)$
SST	$O (N \log N)$	$O (N M L)$	$O (N)$
SAT	$O (N \log N)$	—	$O (N)$
SBC	$O (N \log N)$	—	$O (N)$
SBF	$O (N \log N)$	—	$O (N)$
SDS	$O (N \log N)$	$O (N L \log N)$	$O (N \log N)$
SNMS	$O (N \log N)$	$O (N^{2} L)$	$O (N \log N)$

表1

表2

安全计算协议的通信复杂度"

协议通信轮数/轮	通信开销	协议	通信轮数/轮	通信开销
SMul1	$2 ‖ N ‖$	SAT	6	$10 ‖ N ‖$
SExp1	$‖ N ‖$	SBC	24	$32 ‖ N ‖$
SComp3	$4 ‖ N ‖$	SBF	6	$8 ‖ N ‖$
SRU3	$4 ‖ N ‖$	SDS	1	$‖ N ‖$
SST3	$3 ‖ N ‖$	SNMS	2+13logN	$3 ‖ N ‖ + 18 ‖ N ‖ \log N$

表2

图4

表3

表4

图5

参考文献 32

[1]	LIU Y , MA X , SHU L ,et al. Internet of things for noise mapping in smart cities:state-of-the-art and future directions[J]. IEEE Network, 2020,34(4): 112-118.
[2]	XIONG J , REN J , CHEN L ,et al. Enhancing privacy and availability for data clustering in intelligent electrical service of IoT[J]. IEEE Internet of Things Journal, 2019,6(2): 1530-1540.
[3]	艾瑞咨询. 中国智能物联网（AIoT）白皮书[R].(2020-02-28)[2020-07-20].
	Iresearch.White paper on China’s artificial intelligent and Internet of things (AIoT)[R].(2020-02-28)[2020-07-20].
[4]	XIONG Z , LI W , HAN Q ,et al. Privacy-preserving auto-driving:a GAN-Based approach to protect vehicular camera data[C]// 2019 IEEE International Conference on Data Mining. Piscataway:IEEE Press, 2019: 668-677.
[5]	PéREZ-HERNáNDEZ F , TABIK S , LAMAS A ,et al. Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly:application in video surveillance[J]. Knowledge-Based Systems, 20206(194):105590.
[6]	XIONG J B , CHEN X , YANG Q ,et al. A task-oriented user selection incentive mechanism in edge-aided mobile crowdsensing[J]. IEEE Transactions on Network Science and Engineering, 2019,doi:10.1109/TNSE.2019.2940958.
[7]	CHEN Q , TANG S , YANG Q ,et al. Cooper:cooperative perception for connected autonomous vehicles based on 3d point clouds[C]// 2019 IEEE 39th International Conference on Distributed Computing Systems. Piscataway :IEEE Press, 2019: 514-524.
[8]	CHEN Q , MA X , TANG S ,et al. F-cooper:feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds[C]// 4th ACM/IEEE Symposium on Edge Computing. New York:ACM Press, 2019: 88-100.
[9]	LIN L , LIAO X , JIN H ,et al. Computation offloading toward edge computing[J]. Proceedings of the IEEE, 2019,107(8): 1584-1607.
[10]	WANG X , HAN Y , LEUNG V ,et al. Convergence of edge computing and deep learning:a comprehensive survey[J]. IEEE Communications Surveys ＆ Tutorials, 2020,22(2): 869-904.
[11]	ZHAO Z , ZHENG P , XU S ,et al. Object detection with deep learning:a review[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019,30(11): 3212-3232.
[12]	REN J , GUO Y , ZHANG D ,et al. Distributed and efficient object detection in edge computing:challenges and solutions[J]. IEEE Network, 2018,32(6): 137-143.
[13]	NIKOUEI S , CHEN Y , SONG S ,et al. Real-time human detection as an edge service enabled by a lightweight CNN[C]// 2018 IEEE International Conference on Edge Computing. Piscataway:IEEE Press, 2018: 125-129.
[14]	ZHANG H , ZHANG Z , ZHANG L ,et al. Object tracking for a smart city using IoT and edge computing[J]. Sensors, 2019,19(9): 1987-2009.
[15]	GIRSHICK R , DONAHUE J , DARRELL T ,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2014: 580-587.
[16]	SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[J]. arXiv Preprint,arXiv:1409.1556, 2014
[17]	REN S , HE K , GIRSHICK R ,et al. Faster R-CNN:towards real-time object detection with region proposal networks[C]// Advances in Neural Information Processing Systems. Piscataway:IEEE Press, 2015: 91-99.
[18]	REDMON J , DIVVALA S , GIRSHICK R ,et al. You only look once:unified,real-time object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 779-788.
[19]	XIONG J B , ZHAO M , BHUIYAN M ,et al. An AI-enabled three-party game framework for guaranteed data privacy in mobile edge crowdsensing of IoT[J]. IEEE Transactions on Industrial Informatics, 2019,doi:10.1109/TII.2019.2957130.
[20]	GENTRY C , . Fully homomorphic encryption using ideal lattices[C]// Proceedings of the 41th Annual ACM Symposium on Theory of Computing. New York:ACM Press, 2009: 169-178.
[21]	DOWLIN N , GILAD-BACHRACH R , LAINE K ,et al. Cryptonets:applying neural networks to encrypted data with high throughput and accuracy[C]// International Conference on Machine Learning. New York:ACM Press, 2016: 201-210.
[22]	HESAMIFARD E , TAKABI H , GHASEMI M . CryptoDL:deep neural networks over encrypted data[J]. arXiv Preprint,arXiv:1711.05189, 2017
[23]	JUVEKAR C , VAIKUNTANATHAN V , CHANDRAKASAN A . Gazelle:a low latency framework for secure neural network inference[C]// 27th USENIX Security Symposium. Berkeley:USENIX Association, 2018: 1651-1669.
[24]	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, 2019,doi:10.1109/TDSC.2019.2913362.
[25]	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, 2019(10):1.
[26]	LI F H , LI H , NIU B ,et al. Privacy computing:concept,computing framework,and future development trends[J]. Engineering, 2019,5(6): 981-1192.
[27]	BELANOVI? P , LEESER M , . A library of parameterized floating-point modules and their use[C]// International Conference on Field Programmable Logic and Applications. Berlin:Springer, 2002: 657-666.
[28]	XIONG J B , BI R , ZHAO M ,et al. Edge-assisted privacy-preserving raw data sharing framework for connected autonomous vehicles[J]. IEEE Wireless Communications, 2020,27(3): 24-30.
[29]	BOGDANOV D , LAUR S , WILLEMSON J . Sharemind:a framework for fast privacy-preserving computations[C]// European Symposium on Research in Computer Security. Berlin:Springer, 2008: 192-206.
[30]	BOGDANOV D , NIITSOO M , TOFTETAL T ,et al. High-performance secure multi-party computation for data mining applications[J]. International Journal of Information Security, 2012,11(6): 403-418.
[31]	MA Z , LIU Y , LIU X ,et al. Privacy-preserving outsourced speech recognition for smart IoT devices[J]. IEEE Internet of Things Journal, 2019,6(5): 8406-8420.
[32]	EVERINGHAM M , VANGOOL L , WILLIAMS C ,et al. The PASCAL visual object classes challenge 2007(VOC2007)results[C]// International Conference on Computer Vision. Piscataway:IEEE Press, 2007: 1-24.

网络层	计算开销/ms		倍数/倍	通信开销/MB
网络层	RPN^[17]	SecRPN	倍数/倍	通信开销/MB
3×3卷积	27.3	52.0	1.9	0
安全ReLU	20.9	178.5	8.5	14.84
逐点卷积	0.3	0.6	2.0	0
Softmax	0.9	3.0	3.3	0.39
锚变换	2.0	9.0	4.5	0.65
边界框裁剪	3.2	15.6	4.9	8.35
边界框过滤	1.0	2.1	2.1	2.09
NMS	24.8	59.8	2.4	0.89

网络	计算开销/s	倍数/倍	通信开销/MB
RPN	0.086 2	—	—
SecRCNN	5.776 0	67.0	37.66
SecRPN	0.340 7	4.0	27.21

边缘协作的轻量级安全区域建议网络

Towards edge-collaborative,lightweight and secure region proposal network

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 32

相关文章 15

Metrics

推荐阅读 0

[1]	廖育荣, 王海宁, 林存宝, 李阳, 方宇强, 倪淑燕. 基于深度学习的光学遥感图像目标检测研究进展[J]. 通信学报, 2022, 43(5): 190-203.
[2]	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.
[3]	陈九九, 冯春燕, 郭彩丽, 杨洋, 孙启政, 朱美逸. 车联网中视频语义驱动的资源分配算法[J]. 通信学报, 2021, 42(7): 1-11.
[4]	郭璠, 张泳祥, 唐琎, 李伟清. YOLOv3-A：基于注意力机制的交通标志检测网络[J]. 通信学报, 2021, 42(1): 87-99.
[5]	陈临强,杨全鑫,袁理锋,姚晔,张祯,吴国华. 视频对象移除篡改的时空域定位被动取证[J]. 通信学报, 2020, 41(7): 110-120.
[6]	赵羽,杨洁,刘淼,孙金龙,桂冠. 面向视频监控基于联邦学习的智能边缘计算技术[J]. 通信学报, 2020, 41(10): 109-115.
[7]	于晓涵,陈小龙,关键,黄勇. 雷达海上机动目标高分辨稀疏分数阶模糊函数检测方法[J]. 通信学报, 2019, 40(8): 72-84.
[8]	周鑫,何晓新,郑昌文. 基于图像深度学习的无线电信号识别[J]. 通信学报, 2019, 40(7): 114-125.
[9]	解文华,易本顺,肖进胜,甘良才. 基于像素与子块的背景建模级联算法[J]. 通信学报, 2013, 34(4): 194-200.
[10]	解文华，易本顺，肖进胜，甘良才. 基于像素与子块的背景建模级联算法[J]. 通信学报, 2013, 34(4): 24-200.
[11]	陈哲,王慧斌,沈洁,徐立中. 基于光强—光谱—偏振信息融合的水下目标检测[J]. 通信学报, 2013, 34(3): 192-198.
[12]	王广学,黄晓涛,周智敏. UWB SAR叶簇隐蔽目标差值变化检测中杂波分布建模分析与应用[J]. 通信学报, 2012, 33(2): 76-81.
[13]	黄凤辰,李敏,石爱业,汤敏,徐立中. 受昆虫视觉启发的多光谱遥感影像小目标检测[J]. 通信学报, 2011, 32(9): 88-95.
[14]	陈明生,梁光明,孙即祥,刘东华,赵键. 复杂背景下H.264压缩域运动目标检测算法[J]. 通信学报, 2011, 32(3): 91-97.
[15]	王博,刘德连,张建奇. 基于背景移除的时域目标检测[J]. 通信学报, 2009, 30(7): 67-72.