A Blockchain-Based Fairness Guarantee Approach for Privacy-Preserving Collaborative Training in Computing Force Network

Zhe Sun, Weiping Li, Junxi Liang, Lihua Yin (), Chao Li, Nan Wei, Jie Zhang and Hanyi Wang ()
Additional contact information
Zhe Sun: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Weiping Li: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Junxi Liang: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Lihua Yin: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Chao Li: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Nan Wei: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Jie Zhang: School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Hanyi Wang: China Mobile (Suzhou) Software Technology Co., Ltd., Suzhou 215163, China

Mathematics, 2024, vol. 12, issue 5, 1-22

Abstract: The advent of the big data era has brought unprecedented data demands. The integration of computing resources with network resources in the computing force network enables the possibility of distributed collaborative training. However, unencrypted collaborative training is vulnerable to threats such as gradient inversion attacks and model theft. To address this issue, the data in collaborative training are usually protected by cryptographic methods. However, the semantic meaninglessness of encrypted data makes it difficult to prevent potential data poisoning attacks and free-riding attacks. In this paper, we propose a fairness guarantee approach for privacy-preserving collaborative training, employing blockchain technology to enable participants to share data and exclude potential violators from normal users. We utilize a cryptography-based secure aggregation method to prevent data leakage during blockchain transactions, and employ a contribution evaluation method for encrypted data to prevent data poisoning and free-riding attacks. Additionally, utilizing Shamir’s secret sharing for secret key negotiation within the group, the negotiated key is directly introduced as noise into the model, ensuring the encryption process is computationally lightweight. Decryption is efficiently achieved through the aggregation of encrypted models within the group, without incurring additional computational costs, thereby enhancing the computational efficiency of the encryption and decryption processes. Finally, the experimental results demonstrate the effectiveness and efficiency of our proposed approach.

Keywords: fairness guarantee; collaborative training; computing force network; blockchain; privacy preservation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/5/718/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/5/718/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:5:p:718-:d:1348228

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().