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

arXiv: https://arxiv.org/abs/2011.06458 · HTML: https://mlsanity.com/paper/2011.06458/

## Key facts

- Cited by 6 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training](https://mlsanity.com/paper/2205.01853/) (2022)
- [Proof-of-Guardrail in AI Agents and What (Not) to Trust from It](https://mlsanity.com/paper/2603.05786/) (2026)
- [Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain](https://mlsanity.com/paper/2406.14758/) (2024)
- [DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor Attack](https://mlsanity.com/paper/2308.09487/) (2023)
- [PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarking](https://mlsanity.com/paper/2505.12296/) (2025)
- [TripleWin: Fixed-Point Equilibrium Pricing for Data-Model Coupled Markets](https://mlsanity.com/paper/2511.03368/) (2025)

## Abstract (arXiv)

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.