@incollection{luthi_distributed_2020,
	title = {Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets},
	isbn = {978-3-030-42503-6},
	url = {https://arxiv.org/abs/2002.11000},
	series = {{IFIP} {AICT} Tutorials},
	shorttitle = {Privacy and Identity Management. Data for Better Living},
	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.},
	booktitle = {Privacy and Identity Management. Data for Better Living: {AI} and Privacy: 14th {IFIP} {WG} 9.2, 9.6/11.7, 11.6/{SIG} 9.2.2 International Summer School, Windisch, Switzerland, August 19–23, 2019, Revised Selected Papers},
	publisher = {Springer International Publishing},
	author = {Lüthi, Philipp and Gagnaux, Thibault and Gygli, Marcel},
	editor = {Friedewald, Michael and Önen, Melek and Lievens, Eva and Krenn, Stephan and Fricker, Samuel},
	urldate = {2020-02-26},
	date = {2020},
	langid = {english},
	eprinttype = {arxiv},
	eprint = {2002.11000},
	doi = {10.1007/978-3-030-42504-3},
	keywords = {Computer Science - Cryptography and Security},
}