Privacy-Preserving Methods for Sharing Financial Risk Exposures

TL;DR

This paper introduces cryptographic methods for sharing financial risk exposures that preserve privacy, enabling secure computation of risk metrics without revealing sensitive data, thus enhancing transparency and regulatory oversight.

Contribution

The paper presents novel secure multi-party computation protocols tailored for financial risk data, allowing privacy-preserving aggregation and analysis without trusted third parties.

Findings

Protocols are computationally feasible on realistic data sizes.

Secure computation of risk metrics like correlations and indexes is achievable.

Applications include real-time risk indexes and privacy-preserving financial audits.

Abstract

Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that protect the privacy of all parties involved and without the need for a trusted third party. Our approach employs secure multi-party computation techniques from cryptography in which multiple parties are able to compute joint functions without revealing their individual inputs. In our framework, individual financial institutions evaluate a protocol on their proprietary data which cannot be inverted, leading to secure computations of real-valued statistics such a concentration indexes, pairwise correlations, and other single- and multi-point statistics. The proposed protocols are computationally tractable on realistic sample sizes. Potential financial applications include: the construction of privacy-preserving real-time indexes of bank capital and leverage ratios; the monitoring of delegated portfolio investments; financial audits; and the publication of new indexes of proprietary trading strategies.