A Privacy‐Preserving Approach for Collaborative Optimization Across Company Borders

In modern manufacturing value chains, achieving innovation and optimal performance often requires strong collaboration across organizational boundaries. Tackling the multi-party optimization problems arising from these partnerships necessitates methodologies that can learn from complementary datasets, where each partner provides unique partial features for a shared product or process. Although Data-Driven Evolutionary Optimization (DDEO) is an effective paradigm for solving complex optimization problems, its potential is limited by the need to centralise sensitive proprietary data. Although initial research on privacy-preserving DDEO has emerged, these efforts focus exclusively on horizontally partitioned data and rely on weight aggregation methods that are structurally incompatible with vertically partitioned data. Consequently, secure collaborative optimization across value chains—where data are vertically distributed among partners with distinct features—remains largely unaddressed. To bridge this gap, we propose a novel protocol enabling secure DDEO on feature-partitioned datasets. The architecture protects information in transit via standard encryption and uses Trusted Execution Environments (TEEs) to securely aggregate and process the combined datasets within a hardware-isolated enclave. This creates a versatile, model-agnostic computational environment that accommodates diverse methodologies, allowing practitioners to leverage different algorithmic advantages. Empirical evaluations using four state-of-the-art DDEO algorithms show that the proposed protocol achieves solution quality statistically identical to an insecure centralised baseline. Ultimately, the framework provides a practical solution for multi-party optimization without compromising data confidentiality.