Blockchain Enabled Trustless API Marketplace
This addresses the issue of trust and security for API providers and users in monetizing ML models, though it is an incremental application of blockchain to a specific domain.
The paper tackles the problem of trust in centralized API marketplaces for machine learning services by proposing a blockchain-based decentralized marketplace that divides AI models among multiple cloud vendors to prevent theft and ensure auditability, resulting in a system where cloud vendors cannot collude to steal models and all actions are non-repudiable.
There has been an unprecedented surge in the number of service providers offering a wide range of machine learning prediction APIs for tasks such as image classification, language translation, etc. thereby monetizing the underlying data and trained models. Typically, a data owner (API provider) develops a model, often over proprietary data, and leverages the infrastructure services of a cloud vendor for hosting and serving API requests. Clearly, this model assumes complete trust between the API Provider and cloud vendor. On the other hand, a malicious/buggy cloud vendor may copy the APIs and offer an identical service, under-report model usage metrics, or unfairly discriminate between different API providers by offering them a nominal share of the revenue. In this work, we present the design of a blockchain based decentralized trustless API marketplace that enables all the stakeholders in the API ecosystem to audit the behavior of the parties without having to trust a single centralized entity. In particular, our system divides an AI model into multiple pieces and deploys them among multiple cloud vendors who then collaboratively execute the APIs. Our design ensures that cloud vendors cannot collude with each other to steal the combined model, while individual cloud vendors and clients cannot repudiate their input or model executions.