Abstract
Connecting and exchanging information across organizations becomes increasingly important as supply chains become more complex and expectations with regard to sustainability, transparency and resilience increase. At the same time, organizations are adamant about protecting any competitive advantage which derives from private information about, for example, supplier networks, available inventory or production processes. Technology aimed at enabling information exchange within and across supply chains must therefore ensure high degrees of privacy and control over private information. In light of this, we specify the DibiChain protocol for the discovery and exchange of supply chain information. The protocol prioritizes data minimization in shared data stores, avoidance of persistent user identifiers and anonymous communication with minimal intermediation. We further outline how the DibiChain protocol can serve as the foundation for privacy-preserving supply chain applications, including an anonymous discovery service for GS1 EPCIS event data.
The authors gratefully acknowledge funding through the joint research project DIBICHAIN within the framework of the ReziProK program, which is funded by Germany’s Federal Ministry of Education (funding reference number: 033R241). Apart from the provision of funding, the funding source had no involvement in this study.
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Notes
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For an illustration of how hidden services work, see https://community.torproject.org/onion-services/overview. For the full technical specification, see https://github.com/torproject/torspec/blob/master/rend-spec-v3.txt.
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An important implementation detail is to ensure that users do not overutilize their own Tor node as an introduction point for their addresses, as this would allow observers to correlate them. The prototype implementation of the Connect core module randomly chooses introduction points from a regularly updated list of available Tor nodes.
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Strehle, E., Maurer, M. (2021). The DibiChain Protocol: Privacy-Preserving Discovery and Exchange of Supply Chain Information. In: Bellatreche, L., Chernishev, G., Corral, A., Ouchani, S., Vain, J. (eds) Advances in Model and Data Engineering in the Digitalization Era. MEDI 2021. Communications in Computer and Information Science, vol 1481. Springer, Cham. https://doi.org/10.1007/978-3-030-87657-9_18
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