Ensuring Confidentiality in Supply Chains
Modern supply chains of goods and services rely heavily on close collaborations between the partners within these supply chains. Consequently, there is a demand for IT systems that support collaborations between business partners, for instance, allowing for joint computations for global optimizations (in contrast to local optimizations that each partner can do on their own). Still, businesses are very reluctant to share data or connect their enterprise systems to allow for such joint computation. The topmost factor that businesses name as reason for not collaborating, is their security concern in general and, in particular, the confidentiality of business critical data.
While there are techniques (e.g., homomorphic encryption or secure multi-party computation) that allow joint computations and, at the same time, that are protecting the confidentiality of the data that flows into such a joint computation, they are not widely used. One of the main problems that prevent their adoption is their perceived performance overhead.
In our recently published journal paper [brucker.ea:confidential-supply-chains:2025], which supercedes our earlier conference version [brucker.ea:confidential-lca:2021], we address this problem by an approach that utilized the structure of supply chains by decomposing global computations into local groups, and applying secure multi-party computation within each group. This results in a scalable (resulting in a significant smaller runtime overhead than traditional approaches) and secure (i. e., protecting the confidentiality of data provided by supply chain partners) approach for joint computations within supply chains. We evaluate our approach using life-cycle assessment (LCA) as a case study. Our experiments show that, for instance, secure LCA computations even in supply chains with 15 partners are possible within less than two minutes, while traditional approaches using secure multi-party computation need more than a day.