Replication data for: A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples

Principal Investigator(s): View help for Principal Investigator(s) Raj Chetty; John N. Friedman

Name	File Type	Size	Last Modified
Replication-and-Readme-Package			12/07/2019 04:06:PM
LICENSE.txt	text/plain	14.6 KB	12/07/2019 11:06:AM

Project Citation:

Chetty, Raj, and Friedman, John N. Replication data for: A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples. Nashville, TN: American Economic Association [publisher], 2019. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-07. https://doi.org/10.3886/E116494V1

Project Description

Summary: Building on insights from the differential privacy literature, we develop a simple noise-infusion method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on small samples. Although our method does not offer a formal privacy guarantee, it outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by census tract in the Opportunity Atlas. We provide a step-by-step guide and code to implement our approach.

Scope of Project

JEL Classification:
C81 Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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Published Versions

V1 [2019-12-07]

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