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Conference Papers Year : 2020

Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

Philipp Lüthi
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  • PersonId : 1113512
Thibault Gagnaux
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  • PersonId : 1113513
Marcel Gygli
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  • PersonId : 1113514

Abstract

High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them.
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hal-03378978 , version 1 (14-10-2021)

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Philipp Lüthi, Thibault Gagnaux, Marcel Gygli. Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets. 14th IFIP International Summer School on Privacy and Identity Management (Privacy and Identity), Aug 2019, Windisch, Switzerland. pp.411-426, ⟨10.1007/978-3-030-42504-3_26⟩. ⟨hal-03378978⟩
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