Privacy-Preserving Implementation of an Auction Mechanism for ATFM Slot Swapping

P. Feichtenschlager, K. Schütz, S. Jaburek, C. Schütz, E. Gringinger
Schu23c (2023)
Proceedings of the 23rd Integrated Communications, Navigation and Surveillance Conference (ICNS 2023), Washington D.C., U.S.A., April 18-20, 2023, IEEE Press, 12 pages, DOI: 10.1109/ICNS58246.2023.10124262, 2023. Publication received Best Student Paper Award.

Abstract (English)

Air traffic flow management (ATFM) regulations issued by the EUROCONTROL Network Manager (NM) during periods of reduced capacity in the European air traffic network typically result in flight delays and additional costs for airspace users (AUs). However, not all flights are equally impacted by these regulations, and AUs would like to prioritize flights based on their preferences while protecting the confidentiality of such information. Thus, in the SlotMachine project, we proposed a privacy-preserving marketplace for collaborative optimization of flight lists during ATFM regulations. An auction mechanism incentivizes AUs to participate in the SlotMachine's optimization runs. The proposed implementation of the auction mechanism in a privacy-preserving manner employs a genetic algorithm in combination with multi-party computation (MPC), since a privacy-preserving implementation of a deterministic algorithm would not finish within the time constraints. Experiments using realistic synthetic datasets based on real-world samples demonstrate feasibility of the proposed implementation.

Keywords: air traffic flow management, ATFM regulation, flight prioritization, combinatorial auction, genetic algorithm, multi-party computation