  We study a network revenue management problem where multiple parties agree to
share some of the capacities of the network. This collaboration is performed by
constructing a large mathematical programming model available to all parties.
The parties then use the solution of this model in their own bid-price control
systems. In this setting, the major concern for the parties is the privacy of
their input data and the optimal solutions containing their individual
decisions. To address this concern, we propose an approach based on solving an
alternative data-private model constructed with input masking via random
transformations. Our main result shows that each party can safely recover only
its own optimal decisions after the same data-private model is solved by all
the parties. We also discuss the security of the transformed problem and
consider several special cases where possible privacy leakage would require
attention. Observing that the dense data-private model may take more time to
solve than the sparse original non-private model, we further propose a modeling
approach that introduces sparsity into the data-private model. Finally, we
conduct simulation experiments to support our results.
