2020/11/12 by Jiasi Weng, Weng, Jiasi, Jian Weng +7 · 2 citations
Computer Science · #Blockchain Technology Applications and Security #Cloud Data Security Solutions #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #cs.CR
paper · pdf · doi:10.48550/arxiv.2011.06458
openalex publication_date 2020/11/12 · openalex created_date 2020/11/23 · arxiv created 2021/05/03 · arxiv updated 2021/05/04 · openalex updated_date 2026/07/28
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.