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Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing

文献类型：期刊论文

作者Bai, Yanan1,2; Feng, Yong1; Wul, Wenyuan1
刊名KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
出版日期2021-12-31
卷号15期号:12页码:4345-4363
关键词Privacy Preservation Convolutional Neural Networks Homomorphic Encryption Mobile Cloud Computing Deep Learning
ISSN号1976-7277
DOI10.3837/tiis.2021.12.005
通讯作者Wul, Wenyuan()
英文摘要Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.
资助项目National Key Research and Development Project[2020YFA0712303] ; Chongqing Research Program[cstc2019yszx-jcyjX0003] ; Chongqing Research Program[cstc2020yszx-jcyjX0005] ; Chongqing Research Program[cstc2021yszx-jcyjX0004] ; Guizhou Science and Technology Program[[2020] 4Y056] ; National Natural Science Foundation of China[11771421] ; Youth Innovation Promotion Association of CAS[2018419]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000739929700005
出版者KSII-KOR SOC INTERNET INFORMATION
源URL[http://119.78.100.138/handle/2HOD01W0/15018]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Wul, Wenyuan
作者单位1.Chinese Acad Sci, Chongqing Key Lab Automated Reasoning & Cognit, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Bai, Yanan,Feng, Yong,Wul, Wenyuan. Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing[J]. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS,2021,15(12):4345-4363.
APA Bai, Yanan,Feng, Yong,&Wul, Wenyuan.(2021).Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing.KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS,15(12),4345-4363.
MLA Bai, Yanan,et al."Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing".KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 15.12(2021):4345-4363.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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