  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.
