网络收益管理中数据隐私的掩盖原始和对偶模型
Masking Primal and Dual Models for Data Privacy in Network Revenue Management
摘要：
我们研究了一个协作收入管理问题，其中多个分散的各方同意分享他们的一些能力。这种合作是通过构建一个大型的数学规划模型提供给所有各方。然后，各方在其各自的容量控制系统中使用该模型的解。然而，在这种情况下，各方主要关心的是其输入数据的隐私以及其各自的最优解。我们首先重新制定了一个一般的线性规划模型，可用于广泛的网络收入管理问题。然后，我们解决了数据隐私问题的重新制定的模型，并提出了一种基于解决一个等效的数据隐私模型通过随机变换与输入屏蔽。我们的主要结果表明，解决数据私有模型后，每一方可以安全地访问自己的最优容量控制决策。我们还讨论了在考虑多方设置转换的问题的安全性。我们进行模拟实验来支持我们的结果，并评估所提出的数据私有模型的计算效率。我们的工作提供了一种分析方法和见解，了解如何在网络问题中管理共享资源，同时确保数据隐私。构建和解决协作网络问题需要各方之间的信息交换，这在实践中可能是不可能的。包括数据隐私的分散协作网络收益管理问题与能力共享是新的文献和相关的实践。
英文摘要：
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 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 control decisions. We also discuss the security of the transformed problem in the considered multi-party setting. We conduct simulation experiments 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 the collaborative network problem requires information exchange between parties which 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.
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