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.

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