Cryptology ePrint Archive: Report 2021/939

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

Jiacheng Liang and Wensi Jiang and Songze Li

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 a ML model requested by some model owners, and get compensated for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against 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 owner who intents to evade the payment. OmniLytics is implemented as a smart contract on the Ethereum blockchain to guarantee the atomicity of payment. In OmniLytics, a model owner publishes encrypted initial model on the contract, over which the participating data owners compute gradients using their private data, and securely aggregate the gradients through the contract. Finally, the contract reimburses the data owners, and the model owner decrypts the aggregated model update. We implement a working prototype of OmniLytics on Ethereum, and perform extensive experiments to measure its gas cost and execution time under various parameter combinations, demonstrating its high computation and cost efficiency and strong practicality.

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 18 Jul 2021

Contact author: jliangbb at connect ust hk

Available format(s): PDF | BibTeX Citation

Version: 20210718:150303 (All versions of this report)

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