ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems

TL;DR

ProML is a decentralised platform that uses blockchain technology to securely manage and verify the provenance of machine learning assets across distributed teams, enhancing security and trust without relying on a central authority.

Contribution

It introduces a novel Artefact-as-a-State-Machine architecture and a user-driven provenance capturing mechanism for decentralized ML asset management.

Findings

ProML effectively manages ML provenance with acceptable performance overheads.

The system enhances security against insider threats in distributed ML workflows.

ProML's security model withstands simulated attack scenarios.

Abstract

Large-scale Machine Learning (ML) based Software Systems are increasingly developed by distributed teams situated in different trust domains. Insider threats can launch attacks from any domain to compromise ML assets (models and datasets). Therefore, practitioners require information about how and by whom ML assets were developed to assess their quality attributes such as security, safety, and fairness. Unfortunately, it is challenging for ML teams to access and reconstruct such historical information of ML assets (ML provenance) because it is generally fragmented across distributed ML teams and threatened by the same adversaries that attack ML assets. This paper proposes ProML, a decentralised platform that leverages blockchain and smart contracts to empower distributed ML teams to jointly manage a single source of truth about circulated ML assets' provenance without relying on a third party, which is vulnerable to insider threats and presents a single point of failure. We propose a novel architectural approach called Artefact-as-a-State-Machine to leverage blockchain transactions and smart contracts for managing ML provenance information and introduce a user-driven provenance capturing mechanism to integrate existing scripts and tools to ProML without compromising participants' control over their assets and toolchains. We evaluate the performance and overheads of ProML by benchmarking a proof-of-concept system on a global blockchain. Furthermore, we assessed ProML's security against a threat model of a distributed ML workflow.