OmniLytics+: A Secure, Efficient, and Affordable Blockchain Data Market for Machine Learning through Off-Chain Processing
Songze Li, Mingzhe Liu, Mengqi Chen

TL;DR
OmniLytics+ introduces a decentralized blockchain-based data market that enhances privacy, security, and efficiency for machine learning data sharing through off-chain processing and zero-knowledge proofs.
Contribution
It is the first decentralized data market utilizing zero-knowledge rollups for secure, private, and efficient data validation and aggregation in ML training.
Findings
Reduces gas costs and execution time significantly.
Ensures robustness against malicious data owners.
Demonstrates effective training of large ML models.
Abstract
The rapid development of large machine learning (ML) models requires a massive amount of training data, resulting in booming demands of data sharing and trading through data markets. Traditional centralized data markets suffer from low level of security, and emerging decentralized platforms are faced with efficiency and privacy challenges. In this paper, we propose OmniLytics+, the first decentralized data market, built upon blockchain and smart contract technologies, to simultaneously achieve 1) data (resp., model) privacy for the data (resp. model) owner; 2) robustness against malicious data owners; 3) efficient data validation and aggregation. Specifically, adopting the zero-knowledge (ZK) rollup paradigm, OmniLytics+ proposes to secret share encrypted local gradients, computed from the encrypted global model, with a set of untrusted off-chain servers, who collaboratively generate a…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Privacy-Preserving Technologies in Data
