Privacy-Preserving Network Analytics

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.