Privacy-Preserving Network Analytics

Using financial networks as a backdrop, we develop a new framework for privacy-preserving network analytics. Adopting the debt and equity models of Eisenberg and Noe (2001) and Elliott et al. (2014) as proof of concept, we show how aggregate-level statistics required for stress testing and stability assessment can be derived on real network data, without any individual node revealing its private information to any third party, be it other nodes in the network, or even a central agent. Our work helps bridge the gap between the theoretical models of financial networks that assume agents have full information, and the real world, where information sharing is hindered by privacy and security concerns.