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Summer Institute 2020 Methods Lectures

Differential Privacy for Economists

Lecturers: Daniel Goroff, Dan Kifer, Frauke Kreuter, Ian Schmutte
Supported by the Alfred P. Sloan Foundation
July 17, 2020

The extent to which individual responses to household surveys are protected from discovery by outside parties depends on the summary information released by the collecting government or firm, and on the broader data environment. Rapid decline in the cost of computation, along with a rising number of publicly available data sets, often from private vendors, have increased the risk that a determined party could combine public and private data resources and identify the survey responses of small groups or even individual respondents. Differential privacy is a tool for assessing the trade-off between releasing more granular information based on survey responses and protecting the privacy of survey respondents. These lectures offer an introduction to differential privacy along with examples of its application in settings that range from the collection of data on a small group to the US census.

Complete Index of Methods Lectures



Differential Privacy: Observations for Economists

Daniel Goroff, Alfred P. Sloan Foundation

200
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Introduction to Differential Privacy
Daniel Kifer, Pennsylvania State University

200
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Decisions with Privacy-Protected Data
Ian Schmutte, University of Georgia

200
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Basic Differential Privacy Algorithms and Statistics
Daniel Kifer, Pennsylvania State University

200
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Formal Privacy in Census Data
Ian Schmutte, University of Georgia

200
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Implications of Data Privacy Concerns for Empirical Social Science
Frauke Kreuter, University of Maryland

200
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