Cryptography and the Economics of Supervisory Information: Balancing Transparency and Confidentiality

47 Pages Posted: 12 Nov 2013

See all articles by Mark D. Flood

Mark D. Flood

R. H. Smith School of Business, U. of Maryland

Jonathan Katz

University of Maryland Department of Computer Science

Stephen J. Ong

Federal Reserve Bank of Cleveland

Adam Smith

Pennsylvania State University

Multiple version iconThere are 2 versions of this paper

Date Written: September 4, 2013

Abstract

We elucidate the tradeoffs between transparency and confidentiality in the context of financial regulation. The structure of information in financial contexts creates incentives with a pervasive effect on financial institutions and their relationships. This includes supervisory institutions, which must balance the opposing forces of confidentiality and transparency that arise from their examination and disclosure duties. Prudential supervision can expose confidential information to examiners who have a duty to protect it. Disclosure policies work to reduce information asymmetries, empowering investors and fostering market discipline. The resulting confidentiality/transparency dichotomy tends to push supervisory information policies to one extreme or the other. We argue that there are important intermediate cases in which limited information sharing would be welfare-improving, and that this can be achieved with careful use of new techniques from the fields of secure computation and statistical data privacy. We provide a broad overview of these new technologies. We also describe three specific usage scenarios where such beneficial solutions might be implemented.

Keywords: Cryptography, financial supervision, transparency, confidentiality, differential privacy, secure function evaluation

JEL Classification: D82, C88, G18, G28

Suggested Citation

Flood, Mark D. and Katz, Jonathan and Ong, Stephen J. and Smith, Adam, Cryptography and the Economics of Supervisory Information: Balancing Transparency and Confidentiality (September 4, 2013). Available at SSRN: https://ssrn.com/abstract=2320795 or http://dx.doi.org/10.2139/ssrn.2320795

Mark D. Flood (Contact Author)

R. H. Smith School of Business, U. of Maryland ( email )

College Park
College Park, MD 20742
United States

Jonathan Katz

University of Maryland Department of Computer Science ( email )

College Park
College Park, MD 20742
United States

Stephen J. Ong

Federal Reserve Bank of Cleveland ( email )

East 6th & Superior
Cleveland, OH 44101-1387
United States

Adam Smith

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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