2021/07/12 by Jiacheng Liang, Liang, Jiacheng, Songze Li +7 · 11 citations
Computer Science · #68M14 #68T20 #Blockchain #Blockchain Technology Applications and Security #C.0 #Computer science #Computer security #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #I.2.11 #Machine Learning (cs.LG) #Mobile Crowdsensing and Crowdsourcing #Parallel #Payment #Privacy-Preserving Technologies in Data #Quality (philosophy) #Resilience (materials science) #Smart contract #Software deployment #World Wide Web #acm:68M14 #acm:68T20 #and Cluster Computing (cs.DC) #cs.CR #cs.DC #cs.LG #msc:68M14 #msc:68T20
paper · pdf · doi:10.48550/arxiv.2107.05252
published in arXiv (Cornell University) (Cornell University) · An initial version of the article has been published in International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021(http://federated-learning.org/fl-icml-2021/). This version has been submmited to AAAI'22
openalex publication_date 2021/07/12 · openalex created_date 2021/07/19 · arxiv created 2021/11/15 · arxiv updated 2021/11/16 · openalex updated_date 2026/08/05
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.