  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.
