Paper 2021/939

OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning

Jiacheng Liang, Songze Li, Wensi Jiang, Bochuan Cao, and Chaoyang He

Abstract

We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against the curious model and data owners; 3) resilience to malicious data owners who provide faulty results to poison model training; and 4) resilience to malicious model owners who intend to evade payment. OmniLytics is implemented as a blockchain smart contract to guarantee the atomicity of payment. In OmniLytics, a model owner splits its model into the private and public parts and publishes the public part on the contract. Through the execution of the contract, the participating data owners securely aggregate their locally trained models to update the model owner's public model and receive reimbursement through the contract. We implement a working prototype of OmniLytics on Ethereum blockchain and perform extensive experiments to measure its gas cost, execution time, and model quality under various parameter combinations. For training a CNN on the MNIST dataset, the MO is able to boost its model accuracy from 62% to 83% within 500ms in blockchain processing time.This demonstrates the effectiveness of OmniLytics for practical deployment.

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Published elsewhere. International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021
Keywords
Secure data marketDecentralized machine learningBlockchainEthereum smart contract
Contact author(s)
jliangbb @ connect ust hk
History
2021-09-24: last of 2 revisions
2021-07-13: received
See all versions
Short URL
https://ia.cr/2021/939
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2021/939,
      author = {Jiacheng Liang and Songze Li and Wensi Jiang and Bochuan Cao and Chaoyang He},
      title = {OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning},
      howpublished = {Cryptology ePrint Archive, Paper 2021/939},
      year = {2021},
      note = {\url{https://eprint.iacr.org/2021/939}},
      url = {https://eprint.iacr.org/2021/939}
}
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