ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the
lives of people. A natural requirement for MLaaS is, however, to provide highly
accurate prediction services. To achieve this, current MLaaS systems integrate
and combine multiple well-trained models in their services. Yet, in reality,
there is no easy way for MLaaS providers, especially for startups, to collect
sufficiently well-trained models from individual developers, due to the lack of
incentives. In this paper, we aim to fill this gap by building up a model
marketplace, called as Golden Grain, to facilitate model sharing, which
enforces the fair model-money swapping process between individual developers
and MLaaS providers. Specifically, we deploy the swapping process on the
blockchain, and further introduce a blockchain-empowered model benchmarking
process for transparently determining the model prices according to their
authentic performances, so as to motivate the faithful contributions of
well-trained models. Especially, to ease the blockchain overhead for model
benchmarking, our marketplace carefully offloads the heavy computation and
designs a secure off-chain on-chain interaction protocol based on a trusted
execution environment (TEE), for ensuring both the integrity and authenticity
of benchmarking. We implement a prototype of our Golden Grain on the Ethereum
blockchain, and conduct extensive experiments using standard benchmark datasets
to demonstrate the practically affordable performance of our design.

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