SlotMachine: A Privacy-Preserving Marketplace for Air Traffic Flow Management Slots
Sprache des Vortragstitels:
In air traffic flow management, flight prioritization in case of temporarily reduced capacity of the air traffic network can be considered an application of the assignment problem. Flights are assigned (departure, arrival, or en route) slots according to the respective slot's economic utility for a particular flight, aiming to reduce the overall utility for airlines. Airlines, however, are reluctant to share information regarding the utility of slots for flights, which renders conventional approaches inadequate. This talk presents how a combination of genetic algorithms with multiparty computation (MPC) may serve as the basis for building the SlotMachine platform for flight prioritization under the assumption of an honest-but-curious platform provider. In the proposed method a genetic algorithm generates candidate solutions while a Privacy Engine evaluates the population in each iteration step. The airlines' 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 privacy-preserving manner.