seIMC: A GSW-Based Secure and Efficient Integer Matrix Computation Scheme With Implementation
文献类型：期刊论文
| 作者 | Bai, Yanan1,2; Shi, Xiaoyu3 ; Wu, Wenyuan2; Chen, Jingwei2; Feng, Yong2
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| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2020 |
| 卷号 | 8页码:98383-98394 |
| 关键词 | Encryption Data privacy Cloud computing Lattices Machine learning Computational efficiency Homomorphic encryption matrix computation machine learning GSW encryption scheme big data privacy protection |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2020.2996000 |
| 通讯作者 | Feng, Yong(yongfeng@cigit.ac.cn) |
| 英文摘要 | As atomic operations, secure matrix-based computations using homomorphic encryption (HE) have attracted much attention in cloud-based machine learning. However, most existing secure matrix computation solutions that focus on HE schemes suffer efficiency loss as the size of the matrix, which greatly limits their applications in the big data environment. To address these issues, this paper proposes seIMC, an integer matrix computation scheme based on the Gentry-Sahai-Waters (GSW) scheme, to cope with privacy protection and secure computation of large-scale data. In detail, we translate the GSW scheme to encrypt an integer matrix modulo (i.e., a large positive integer), and homomorphically compute matrix addition and multiplication, which is a natural extension of HAO scheme. Besides, the correctness and security analysis of seIMC are shown, and complexity analysis is also given in this study. Furthermore, the proposed schemes are implemented, including public-key encryption and private-key encryption schemes. Compared with existing secure matrix computation schemes, the proposed scheme performs better in execution time. Finally, seIMC is applied to solve the problem of the number of ways in which any two participants make friends through steps in an encrypted social network. Experiments show that when the cloud server processes an integer matrix of 1000 people with a security level of 90, namely, 1 million data volumes, it only takes approximately 1.9 minutes for each homomorphic matrix multiplication. Hence, the practicality of the proposed seIMC in privacy protection under a big data environment is highly proven. |
| 资助项目 | National Natural Science Foundation of China[11671377] ; Chongqing Science and Technology Program[cstc2018jcyj-yszxX0002] ; Chongqing Science and Technology Program[cstc2019yszx-jcyjX0003] ; Guizhou Science and Technology Program[[2020]4Y056] ; Chongqing Research Program of the Key Standard Technologies Innovation of Key Industries[cstc2017zdcy-zdyfX0076] ; Chongqing Research Program of Technology Innovation and Application[2019jscx-zdztzxX0019] ; Chongqing Natural Science Foundation[cstc2019jcyj-msxmX0638] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000541144400009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/11261]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Feng, Yong |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Automated Reasoning & Cognit, Chongqing 400714, Peoples R China 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Bai, Yanan,Shi, Xiaoyu,Wu, Wenyuan,et al. seIMC: A GSW-Based Secure and Efficient Integer Matrix Computation Scheme With Implementation[J]. IEEE ACCESS,2020,8:98383-98394. |
| APA | Bai, Yanan,Shi, Xiaoyu,Wu, Wenyuan,Chen, Jingwei,&Feng, Yong.(2020).seIMC: A GSW-Based Secure and Efficient Integer Matrix Computation Scheme With Implementation.IEEE ACCESS,8,98383-98394. |
| MLA | Bai, Yanan,et al."seIMC: A GSW-Based Secure and Efficient Integer Matrix Computation Scheme With Implementation".IEEE ACCESS 8(2020):98383-98394. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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