Efficient Model Collaborative Training and Sharing Scheme of Blockchain Based on Hybrid Privacy

ZHANG Cui; YANG Hui; WANG Hanning; WANG Jiang; ZENG Chuangzhan; LI Rongkuan

doi:10.11999/JEIT221104

Volume 45 Issue 3

Mar. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(3): 775-783

ZHANG Cui, YANG Hui, WANG Hanning, WANG Jiang, ZENG Chuangzhan, LI Rongkuan. Efficient Model Collaborative Training and Sharing Scheme of Blockchain Based on Hybrid Privacy[J]. Journal of Electronics & Information Technology, 2023, 45(3): 775-783. doi: 10.11999/JEIT221104

Citation:

ZHANG Cui, YANG Hui, WANG Hanning, WANG Jiang, ZENG Chuangzhan, LI Rongkuan. Efficient Model Collaborative Training and Sharing Scheme of Blockchain Based on Hybrid Privacy[J]. Journal of Electronics & Information Technology, 2023, 45(3): 775-783. doi: 10.11999/JEIT221104

Citation:

ZHANG Cui, YANG Hui, WANG Hanning, WANG Jiang, ZENG Chuangzhan, LI Rongkuan. Efficient Model Collaborative Training and Sharing Scheme of Blockchain Based on Hybrid Privacy[J]. Journal of Electronics & Information Technology, 2023, 45(3): 775-783. doi: 10.11999/JEIT221104

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Efficient Model Collaborative Training and Sharing Scheme of Blockchain Based on Hybrid Privacy

doi: 10.11999/JEIT221104

1.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
Unit 61932, Beijing 100000, China
3.
Dianke Cloud (Beijing) Technology Co., Ltd, Beijing 100043, China

Funds: The National Natural Science Foundation of China (62122015)

Received Date: 2022-08-23
Rev Recd Date: 2022-12-30

Available Online: 2023-01-05

Publish Date: 2023-03-10

Abstract

Abstract

Considering the problems of inefficiency and privacy leakage faced by the blockchain-based federated learning data sharing platform under massive data, an efficient model collaborative training and sharing scheme of blockchain based on hybrid privacy is proposed. In this scheme, a similarity-based training member selection algorithm according to Euclidean distance is first designed to select training members, forming a federated community, that is, to improve the efficiency and effect of training by selecting a small number of high-matching training nodes. Then, combined with threshold homomorphic encryption and differential privacy, a model collaborative training and sharing scheme based on hybrid privacy technology is constructed to ensure the privacy in the training and sharing process. The experimental results and system implementation show that the proposed scheme can achieve efficient training and data sharing under privacy protection while ensuring the accuracy of the training results.
- Blockchain,
- Federated learning,
- Threshold homomorphic encryption,
- Differential privacy

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References(20)

References

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