Privacy-Preserving Network Analytics
为金融网络模型开发了隐私保护框架，利用密码学原理在不泄露节点隐私的前提下计算网络聚合统计量，用于稳定性评估和压力测试，弥合了理论假设与现实隐私障碍之间的差距。
We develop a new privacy-preserving framework for a general class of financial network models, leveraging cryptographic principles from secure multiparty computation and decentralized systems. We show how aggregate-level network statistics required for stability assessment and stress testing can be derived from real data without any individual node revealing its private information to any outside party, be it other nodes in the network, or even a central agent. Our work bridges the gap between established theories of financial network contagion and systemic risk that assume agents have full network information and the real world where information sharing is hindered by privacy and security concerns. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4582 .