Masking primal and dual models for data privacy in network revenue management

We study a collaborative revenue management problem where multiple decentralized parties agree to share some of their capacities. This collaboration is performed by constructing a large mathematical programming model that is available to all parties. The parties then use the solution of this model in their own capacity control systems. In this setting, however, the major concern for the parties is the privacy of their input data, along with their individual optimal solutions. We first reformulate a general linear programming model that can be used for a wide range of network revenue management problems. Then we address the data privacy concern of the reformulated model and propose an approach based on solving an equivalent data-private model constructed with input masking via random transformations. Our main result shows that, after solving the data-private model, each party can safely access only its own optimal capacity allocation decisions. We also discuss the security of the transformed problem in the considered multi-party setting. Simulation experiments are conducted to support our results and evaluate the computational efficiency of the proposed data-private model. Our work provides an analytical approach and insights on how to manage shared resources in a network problem while ensuring data privacy. Constructing and solving a collaborative network problem requires information exchange between parties that may not be possible in practice. Including data privacy in decentralized collaborative network revenue management problems with capacity sharing is new to the literature and relevant to practice.