Golden Grain: Building a Secure and Decentralized Model Marketplace for MLaaS

【Author】 Weng, Jiasi; Weng, Jian; Cai, Chengjun; Huang, Hongwei; Wang, Cong

【Source】IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING

【影响因子】6.791

【Abstract】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 article, 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.

【Keywords】Benchmark testing; Blockchain; Computational modeling; Data models; Smart contracts; Predictive models; Urban areas; ML-as-a-service; blockchain; marketplace; trusted execution environment

【发表时间】2022 44805

【收录时间】2022-09-15

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-机器学习