A Distributed Architecture for Privacy-Preserving Optimization Using Genetic Algorithms and Multi-party Computation

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.