SlotMachine - A Privacy-Preserving Marketplace for Slot Management

Project duration
October 2020 - December 2022
Project website
Funded by
EU Horizon 2020, SESAR JU
Project number

Short description

The aviation industry is confronted with rising passenger numbers and increased flight volume in the face of limited resources at airports and in the air. At the same time, airlines struggle with increased cost pressure from an increasing number of market participants while the highest safety standards demand compliance with complex processes. One promising area of optimization is the allocation of Air Traffic Flow Management (ATFM) slots. ATFM slots are issued by the EUROCONTROL Network Manager in times of increased flight traffic, regulating time of departure, exact execution of the flight route, and time of landing.

Until now, ATFM slots have only been subject to intra-airline swaps, used by airlines to prioritize expensive flights and thus minimize overall costs. Reasons for different costs of individual flights are, for example, the provisioning of connecting flights for passengers or work time restrictions for crew members. Airlines want to keep the cost structure of their flights confidential, as they fear a competitive disadvantage when disclosed. This desire for confidentiality has hampered slot swapping between different airlines.

SlotMachine will employ blockchain technology and secure multi-party computation to extend the existing UDPP solution with the possibility to keep private the participating airlines’ confidential information such as the cost structure of flights. Technology will allow for secure, auditable transactions without the need for a central broker where stakeholders will be able to enter slot swapping transactions without disclosing information to other participants.

By demonstrating the feasibility of a privacy-preserving platform for swapping ATFM slots, the foundation can be laid for the development of a product that will be an essential element in the aviation industry in the future. It contributes to better use of existing resources at airports, higher efficiency of airlines, lower emissions, and shorter delays for passengers.

Project SlotMachine - Video
CORDIS - EU research results and details of the project

Project partners
Frequentis AG

Project team
Michael Schrefl (DKE)

T. Lorünser, C. Schütz, E. Gringinger:
SlotMachine - A Privacy-preserving Marketplace for Slot Management
In: ERCIM News, Vol. 126, Special Issue on Privacy Preserving Computation, July 2021, ERCIM EEIG (, ISSN 0926-4981, 2 pages, 2021.
C. Schütz, E. Gringinger, N. Pilon, T. Lorünser:
A Privacy-Preserving Marketplace for Air Traffic Flow Management Slot Configuration
In: Proceedings of the 40th Digital Avionics Systems Conference (DASC 2021), San Antonio, Texas, USA, October 3-7, 2021, IEEE Press, ISBN 978-1-6654-3420-1/21, DOI: 10.1109/DASC52595.2021.9594401, 2021.
E. Gringinger, S. Ruiz, C. Schütz:
Business and Economic Concepts for a Privacy-Preserving Marketplace for ATFM Slots
In: Proceedings of the 22nd Integrated Communications, Navigation and Surveillance Systems Conference (ICNS 2022), Washington D.C., U.S.A., April 5-7, 2022, IEEE Press, ISBN: 978-1-6654-8419-0, DOI: 10.1109/ICNS54818.2022.9771484, 8 pages, 2022.
S. Jaburek:
Experimentelle Evaluierung heuristischer Suchalgorithmen zur Optimierung von Abflugreihenfolgen im Air Traffic Flow Management
(Master Thesis, 2021)
C. Schütz, S. Ruiz, E. Gringinger, C. Fabianek, T. Lorünser:
An Auction-Based Mechanism for a Privacy-Preserving Marketplace for ATFM Slots
In: Proceedings of the 33rd Congress of the International Council of the Aeronautical Sciences (ICAS 2022), Stockholm, Sweden, September 4-9, 2022, ISSN 2958-4647, 14 pages, 2022.
C. Schütz, T. Lorünser, S. Jaburek, K. Schütz, F. Wohner, R. Karl, E. Gringinger:
A Distributed Architecture for Privacy-Preserving Optimization Using Genetic Algorithms and Multi-party Computation
In: Proceedings of the International Conference on Cooperative Information Systems (CoopIS 2022), Bozen, Italy, October 4-7, 2022. Collocated with EDOC 2022. Editoren: Sellami, M., Ceravolo, P., Reijers, H.A., Gaaloul, W., Panetto, H., Springer Verlag, Lecture Notes in Computer Science (LNCS), Vol. 13591, DOI:, ISBN 978-3-031-17833-7, pp. 168-185, 2022.
C. Schütz, S. Jaburek:
Specification of Evolutionary Algorithm under Constraints Post-Filtering/Ordering (EU-H2020-Project SlotMachine)
Institute report SLOTMACHINE D4.2, December 2022.
C. Schütz, S. Jaburek:
Report on State-of-the-Art of Relevant Concepts (EU-H2020-Project SlotMachine)
Institute report SLOTMACHINE D4.1, July 2022.
M. Carre, E. Gringinger, C. Schütz, T. Lorünser:
Exploitation Plan and Return on Investment Analysis (EU-H2020-Project SlotMachine)
Institute report SLOTMACHINE D5.2, December 2022.
C. Schütz, T. Lorünser, T. Obritzhauser, C. Fabianek, E. Gringinger:
Requirements Specification (EU-H2020-Project SlotMachine)
Institute report SLOTMACHINE D2.1, October 2022.
C. Schütz, T. Lorünser, T. Obritzhauser, C. Fabianek, E. Gringinger:
System Design Document (EU-H2020-Project SlotMachine)
Institute report SLOTMACHINE D2.2, December 2022.
P. Feichtenschlager, K. Schütz, S. Jaburek, C. Schütz, E. Gringinger:
Privacy-Preserving Implementation of an Auction Mechanism for ATFM Slot Swapping
In: 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.
K. Schütz, C. Schütz, S. Jaburek:
Privacy-Preserving Implementation of Local Search Algorithms for Collaboratively Solving Assignment Problems in Time-Critical Contexts
In: Proceedings of the IEEE 2023 Congress on Evolutionary Computation (CEC 2023), Chicago, IL, U.S.A., July 1-5, 2023, IEEE Press, 10 pages, ISBN 979-8-3503-1458-8, DOI: 10.1109/CEC53210.2023.10253978, 2023.