Contents 1 The Transparency - Confidentiality Tradeoff 3 2 Costs and Benefits

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. The authors gratefully acknowledge helpful comments from and discussion with Peter Bloniarz, Jill Cetina, Greg Duffee, Greg Feldberg, Benjamin Kay, Matt McCormick, Claudio Orlandi, Mallesh Pai, Bill Treacy, and Valerie Wells, as well as participants at research workshops at Penn State’s Center for Financial Stability (March 2012), the Office of Financial Research (April 2013), the California Institute of Technology (May 2013), and the Federal Reserve (May 2013). Any remaining errors or omissions are the responsibility of the authors alone. Adam Smith’s work at Penn State was supported in part by National Science Foundation awards #0941553 and #0747294. Jonathan Katz’s work was supported in part by National Science Foundation award #1111599. Views and opinions expressed are those of the authors and do not necessarily represent official OFR or Treasury positions or policy. The views stated herein are those of the authors and are not necessarily those of the Federal Reserve Bank of Cleveland or of the Board of Governors of the Federal Reserve System. Comments and suggestions are welcome and should be directed to the authors. ∗Office of Financial Research, U.S. Department of the Treasury, Washington, D.C. †Computer Science Department, University of Maryland, College Park, Md. ‡Federal Reserve Bank of Cleveland, Cleveland, Ohio §Computer Science and Engineering Department, Pennsylvania State University, University Park, Pa.