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PRepChain: A versatile privacy-preserving reputation system for dynamic supply chain environments
(2025)
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.
The establishment, expansion, and operation of reliable value-creation networks present an increasing challenge for manufacturing companies, given the growing volatility of the market environment in which they operate. For example, the development of new business areas, mass customization, or the disruption of supply chains frequently necessitates the establishment of partnerships with new suppliers, both short- and long-term. The utilization of supplier key performance indicators (KPIs) can facilitate the selection of new business partners, as they provide a quick and objective indication of their reliability. Nevertheless, access to potentially sensitive KPIs, such as a supplier's on-time delivery performance, is currently mainly limited to existing supplier relationships and not made available to other companies. This paper presents a coordinated approach for supplier rating systems, thereby enabling the privacy-aware exchange of supplier KPIs across organizations and exemplifies it using an application in the “Internet of Production”.
Specifically, we conduct interdisciplinary research by formulating the requirements from a business perspective (supply chain design, trust in data sharing, and business models) and evaluating promising solutions from a technical perspective (information security, data quality, data sovereignty, and collaboration). This approach enables the combination of state-of-the-art technology with the evolving requirements of stakeholders, thus creating new paths for exploiting inter-organizational supply chain rating.
Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these concepts are not yet in use in business-to-business environments. In this paper, (1) we thus outline which specific challenges privacy-cautious stakeholders in volatile supply chain networks introduce, (2) give an overview of the diverse landscape of privacy-preserving reputation systems and their properties, and (3) based on well-established concepts from supply chain information systems and cryptography, we further propose an initial concept that accounts for the aforementioned challenges by utilizing fully homomorphic encryption. For future work, we identify the need of evaluating whether novel systems address the supply chain-specific privacy and confidentiality needs.
Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010–2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points.
