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

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 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.

References in corpus (14)

• Towards Deep Learning Models Resistant to Adversarial Attacks
• Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
• Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution
• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
• Adversarial Examples for Evaluating Reading Comprehension Systems
• Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
• Chiron: Privacy-preserving Machine Learning as a Service
• Adversarial Examples Are a Natural Consequence of Test Error in Noise
• TensorFlow-Serving: Flexible, High-Performance ML Serving
• Preventing Your Faults From Telling Your Secrets: Defenses Against Pigeonhole Attacks
• DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation
• Model-based Pricing for Machine Learning in a Data Marketplace
• Confidential Attestation: Efficient in-Enclave Verification of Privacy Policy Compliance