Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms

Computer Science > Neural and Evolutionary Computing [Submitted on 20 May 2026] Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms Sebastian Gruber, Tobias Harzfeld, Christoph G. Schuetz, Florian Wohner, Thomas Lorünser In distributed optimization, multiple parties collaborate to find an optimal solution to a problem. Privacy-preserving distributed optimization uses techniques, such as secure multi-party computation (MPC), to protect the private inputs of each party. In time-critical settings, the runtime overhead introduced by privacy-preserving computations may prevent the optimization from finishing within the deadline. This paper presents an approach for privacy-preserving distributed optimization in time-critical settings that combines evolutionary algorithms for solution search and MPC for the evaluation of solutions. The approach reduces the impact of privacy-preserving computations on runtime and allows to return solution within the deadline. Obfuscation of evaluation results provides additional protection for private inputs from an honest-but-curious platform provider, but introduces a potential trade-off between protection and solution quality. This trade-off is investigated in experiments using a genetic algorithm for both the single-objective assignment problem and the traveling salesperson problem, as well as NSGA-II for the multi-objective assignment problem. Comments: 30 pages, 9 figures, 14 tables; links to experimental data Subjects: Neural and Evolutionary Computing (cs.NE); Cryptography and Security (cs.CR) Cite as: arXiv:2605.20944 [cs.NE]   (or arXiv:2605.20944v1 [cs.NE] for this version)   https://doi.org/10.48550/arXiv.2605.20944 Focus to learn more Submission history From: Christoph Schuetz [view email] [v1] Wed, 20 May 2026 09:29:50 UTC (4,856 KB) Access Paper: HTML (experimental) view license Current browse context: cs.NE < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CR References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)