Privacy-Preserving Network Analytics

Abstract

We develop a new privacy-preserving analytics framework for a general class of financial network models, leveraging recent advances in cryptography and decentralized systems (secure multi-party computation). We show how aggregate-level statistics required for stability assessment and stress testing can be derived from 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 bridges the gap between established theories of financial network contagion and systemic risk, that assume agents have full information, and the real world, where information sharing is hindered by privacy and security concerns.

Keywords: financial networks, multiparty computation, privacy preservation, fintech

JEL Classification: E6, G2, L5, O3, C6