Abstract
In many industries, competitors are required to cooperate in order to conduct optimizations, e.g., to solve an assignment problem. For example, in air traffic flow management (ATFM), flight prioritization in case of temporarily reduced capacity of the air traffic network is an instance of the assignment problem. Participants, however, are typically reluctant to share sensitive information regarding their preferences for the optimization, which renders conventional approaches to optimization inadequate. This paper proposes a method for combining genetic algorithms with multi-party computation (MPC) as the basis for building a platform for optimizing the assignment of resources to different agents under the assumption of an honest-but-curious platform provider; the method is illustrated on the ATFM use case. In the proposed method a genetic algorithm iteratively generates a population of candidate solutions to the assignment problem while a Privacy Engine component evaluates the population in each iteration step. The participants’ private inputs are kept from competitors and not even the platform provider knows those inputs, receiving only encrypted input which is processed by MPC nodes in a way that preserves the secrecy of the inputs.
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Castelli, L., Pesenti, R., Ranieri, A.: The design of a market mechanism to allocate air traffic flow management slots. Transp. Res. Part C Emerg. Technol. 19(5), 931–943 (2011). https://doi.org/10.1016/j.trc.2010.06.003
Cramer, R., Damgård, I.B., Nielsen, J.B.: Secure Multiparty Computation. Cambridge University Press, Cambridge (2015)
Doerner, J., Evans, D., Shelat, A.: Secure stable matching at scale. In: Weippl, E.R., Katzenbeisser, S., Kruegel, C., Myers, A.C., Halevi, S. (eds.) ACM Conference on Computer and Communications Security, pp. 1602–1613 (2016)
Franklin, M., Gondree, M., Mohassel, P.: Improved efficiency for private stable matching. In: Abe, M. (ed.) CT-RSA 2007. LNCS, vol. 4377, pp. 163–177. Springer, Heidelberg (2006). https://doi.org/10.1007/11967668_11
Funke, D., Kerschbaum, F.: Privacy-preserving multi-objective evolutionary algorithms. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6239, pp. 41–50. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15871-1_5
Gale, D., Shapley, L.S.: College admissions and the stability of marriage. Am. Math. Mon. 120(5), 386–391 (2013)
Golle, P.: A private stable matching algorithm. In: Di Crescenzo, G., Rubin, A. (eds.) FC 2006. LNCS, vol. 4107, pp. 65–80. Springer, Heidelberg (2006). https://doi.org/10.1007/11889663_5
Keller, M.: MP-SPDZ: a versatile framework for multi-party computation. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (2020). https://doi.org/10.1145/3372297.3417872
Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q. 2(1–2), 83–97 (1955)
Lewi, K., Wu, D.J.: Order-revealing encryption: new constructions, applications, and lower bounds. Cryptology ePrint Archive, Report 2016/612 (2016)
Lorünser, T., Wohner, F., Krenn, S.: A verifiable multiparty computation solver for the assignment problem and applications to air traffic management (2022). https://doi.org/10.48550/ARXIV.2205.03048
Menezes, A., van Oorschot, P., Vanstone, S.: Handbook of Applied Cryptography. CRC Press, New York (1997). https://doi.org/10.1201/9780429466335
Pilon, N., Guichard, L., Bazso, Z., Murgese, G., Carré, M.: User-driven prioritisation process (UDPP) from advanced experimental to pre-operational validation environment. J. Air Transp. Manag. 97, 102124 (2021). https://doi.org/10.1016/j.jairtraman.2021.102124
Sadegh Riazi, M., Songhori, E.M., Sadeghi, A.R., Schneider, T., Koushanfar, F.: Toward practical secure stable matching. In: Proceedings on Privacy Enhancing Technologies Symposium (PoPETs), pp. 62–78 (2017)
Schuetz, C.G., Gringinger, E., Pilon, N., Lorünser, T.: A privacy-preserving marketplace for air traffic flow management slot configuration. In: 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), pp. 1–9 (2021). https://doi.org/10.1109/DASC52595.2021.9594401
Schuetz, C.G., et al.: A distributed architecture for secrecy-preserving optimization using genetic algorithms and multi-party computation - Appendix. http://files.dke.uni-linz.ac.at/publications/schu22c/appendix.pdf
Simon, D.: Evolutionary Optimization Algorithms. Wiley, New York (2013)
Wilhelmstötter, F.: Jenetics library user’s manual 7.1 (2022). https://jenetics.io/manual/manual-7.1.0.pdf
Wüller, S., Vu, M., Meyer, U., Wetzel, S.: Using secure graph algorithms for the privacy-preserving identification of optimal bartering opportunities. In: Proceedings of the 2017 Workshop on Privacy in the Electronic Society, pp. 123–132 (2017)
Acknowledgements
This work was conducted as part of the SlotMachine project. This project received funding from the SESAR Joint Undertaking under grant agreement No 890456 under the European Union’s Horizon 2020 research and innovation program. The views expressed in this paper are those of the authors.
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Schuetz, C.G. et al. (2022). A Distributed Architecture for Privacy-Preserving Optimization Using Genetic Algorithms and Multi-party Computation. In: Sellami, M., Ceravolo, P., Reijers, H.A., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2022. Lecture Notes in Computer Science, vol 13591. Springer, Cham. https://doi.org/10.1007/978-3-031-17834-4_10
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