Full-text downloads (blue) and page views (gray)

PRepChain: A versatile privacy-preserving reputation system for dynamic supply chain environments

  • Despite their significant added value in the context of consumer-oriented e-commerce, reputation systems have seen limited adoption in other business settings and models these days. Yet, reliable reputation scores are essential in such settings for easing the establishment of new business relationships—an aspect that is particularly crucial in dynamic supply chain environments, where business partners change frequently. Existing approaches, however, usually target other application domains and fall short in addressing the specific challenges of dynamic supply chains—especially with respect to reliability (incl. availability) and privacy preservation (incl. confidentiality). To close this research gap and to support novel directions in this important research area, we propose PRepChain, our highly-configurable approach that leverages fully homomorphic encryption and distributed competences to provide businesses with a versatile reputation-enriched ecosystem. PRepChain is specifically designed to operate in dynamic environments by also offering a trade-off between data availability and confidentiality guarantees. We make contributions in four primary directions: (i) It offers performant privacy preservation even in large-scale settings, (ii) ensures availability of computed reputation scores, (iii) seamlessly integrates with existing supply chain information systems, and (iv) in addition to subjective reputation scores, it also supports reliably-calculated, i.e., objective, ones, thereby strengthening the reliability of third-party-sourced information. Our evaluation of PRepChain documents its performance—based on a real-world use case—, security, and privacy preservation, hence, its applicability. We conclude that it is indeed destined for practical deployments in modern supply networks.

Export metadata

Additional Services

Search Google Scholar

Statistics

Access statistics
Metadaten
Author:Jan PennekampORCiD, Lennart BaderORCiD, Emildeon ThevarajORCiD, Stefanie BerningerORCiD, Martin PerauORCiD, Tobias SchröerORCiDGND, Wolfgang BoosORCiDGND, Salil S. KanhereORCiD, Klaus WehrleORCiD
DOI:https://doi.org/10.1016/j.future.2025.108024
ISSN:0167-739X
Parent Title (English):Future Generation Computer Systems
Publisher:Elsevier BV
Document Type:Article
Language:English
Date of Publication (online):2025/08/11
Date of first Publication:2025/08/11
Release Date:2025/08/18
Tag:02
Confidentiality; Homomorphic encryption; Subjective and objective ratings; Trust; Unlinkability
Volume:175
Article Number:108024
Page Number:14 S.
Note:
Acknowledgments:
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Germany under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612 and the Alexander von Humboldt (AvH) Foundation, Germany.

Data availability:
Due to the sensitive nature of our real-world use case data, we can-not disclose the manufacturing company’s name nor share the original data used for deriving the reputation scores. Our implementation can be used with arbitrary data. Accordingly, we include exemplary data on GitHub: https://github.com/COMSYS/PRepChain.
Institute / Department:FIR e. V. an der RWTH Aachen
Produktionsmanagement
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International