2020/02/25 by Philipp Lüthi, Lüthi, Philipp, Thibault Gagnaux +3 · 1 citation
Computer Science · Decision Sciences · #Blockchain Technology Applications and Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Privacy-Preserving Technologies in Data #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2002.11000
openalex publication_date 2020/02/25 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
High availability of data is responsible for the current trends in Artificial\nIntelligence (AI) and Machine Learning (ML). However, high-grade datasets are\nreluctantly shared between actors because of lacking trust and fear of losing\ncontrol. Provenance tracing systems are a possible measure to build trust by\nimproving transparency. Especially the tracing of AI assets along complete AI\nvalue chains bears various challenges such as trust, privacy, confidentiality,\ntraceability, and fair remuneration. In this paper we design a graph-based\nprovenance model for AI assets and their relations within an AI value chain.\nMoreover, we propose a protocol to exchange AI assets securely to selected\nparties. The provenance model and exchange protocol are then combined and\nimplemented as a smart contract on a permission-less blockchain. We show how\nthe smart contract enables the tracing of AI assets in an existing industry use\ncase while solving all challenges. Consequently, our smart contract helps to\nincrease traceability and transparency, encourages trust between actors and\nthus fosters collaboration between them.\n