Cryptology ePrint Archive: Report 2021/939

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

Jiacheng Liang and Songze Li and Wensi Jiang and 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.

Category / Keywords: applications / Secure data market; Decentralized machine learning; Blockchain; Ethereum smart contract

Original Publication (in the same form): International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021

Date: received 11 Jul 2021, last revised 24 Sep 2021

Contact author: jliangbb at connect ust hk

Available format(s): PDF | BibTeX Citation

Version: 20210924:075337 (All versions of this report)

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