A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples

Raj Chetty and John Friedman

AEA Papers and Proceedings, 2019, vol. 109, 414-20

Abstract: 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.

JEL-codes: C81 (search for similar items in EconPapers)
Date: 2019
Note: DOI: 10.1257/pandp.20191109
References: Add references at CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://www.aeaweb.org/doi/10.1257/pandp.20191109 (application/pdf)
https://www.aeaweb.org/doi/10.1257/pandp.20191109.ds (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.

Related works:
Working Paper: A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aea:apandp:v:109:y:2019:p:414-20

Ordering information: This journal article can be ordered from
https://www.aeaweb.org/subscribe.html

Access Statistics for this article

AEA Papers and Proceedings is currently edited by William Johnson and Kelly Markel

More articles in AEA Papers and Proceedings from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().