Cryptographic Approaches for Privacy-Preserving Machine Learning

Han JIANG; Yiran LIU; Xiangfu SONG; Hao WANG; Zhihua ZHENG; Qiuliang XU

doi:10.11999/JEIT190887

Volume 42 Issue 5

Jun. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(5): 1068-1078

Han JIANG, Yiran LIU, Xiangfu SONG, Hao WANG, Zhihua ZHENG, Qiuliang XU. Cryptographic Approaches for Privacy-Preserving Machine Learning[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1068-1078. doi: 10.11999/JEIT190887

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Han JIANG, Yiran LIU, Xiangfu SONG, Hao WANG, Zhihua ZHENG, Qiuliang XU. Cryptographic Approaches for Privacy-Preserving Machine Learning[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1068-1078. doi: 10.11999/JEIT190887

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Cryptographic Approaches for Privacy-Preserving Machine Learning

doi: 10.11999/JEIT190887

1.
School of Software, Shandong University, Jinan 250101, China
2.
School of Information Science and Technology, Shandong Normal University, Jinan 250358, China

Funds: The National Natural Science Foundation of China (61632020, 61572294); The Natural Science Foundation of Shandong Province (ZR2017MF021); The Major Innovation Project of Science and Technology of Shandong Province (2018CXGC0702); The Funds Project of National Independent Innovation Demonstration Zone in Shandong Peninsula (S190101010001)

Received Date: 2019-11-06
Rev Recd Date: 2020-03-08

Available Online: 2020-04-03

Publish Date: 2020-06-04

Abstract

Abstract

The characteristics of the new generation of artificial intelligence technology are shown as follows: with the help of GPU computing, cloud computing and other high-performance distributed computing capabilities, machine learning algorithms represented by deep learning algorithms are used for learning and training on big data to simulate, extend and expand human intelligence. Different data sources and computing physical locations make the current machine learning face serious privacy leakage problem, so the Privacy Protection of Machine (PPM) Learning has become a widely concerned research area. Using cryptography technology to solve the problem of privacy in machine learning is an important technology to protect the privacy of machine learning. Cryptographic tools used in privacy-preserving machine learning are introduced, such as general Secure Multi-Party Computing (SMPC), privacy protection set operation and Homomorphic Encryption (HE), describes the status and developments applying the tools to solving the problems of privacy protection in various stages of machine learning, such as data processing, model training, model testing, and data prediction.

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References(43)

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