Volume 15, Issue 6 e1615
Overview
Open Data

The promise and limitations of formal privacy

Aaron R. Williams

Aaron R. Williams

Income and Benefits Policy Center, Office of Technology and Data Science, Urban Institute, Washington, District of Columbia, USA

Contribution: Conceptualization (equal), Visualization (equal), Writing - original draft (lead), Writing - review & editing (equal)

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Claire McKay Bowen

Corresponding Author

Claire McKay Bowen

Center on Labor, Human Services, and Population, Office of Technology and Data Science, Urban Institute, Washington, District of Columbia, USA

Correspondence

Claire McKay Bowen, Center on Labor, Human Services, and Population, Office of Technology and Data Science, Urban Institute, Washington, DC, 20024, USA.

Email: [email protected]

Contribution: Conceptualization (equal), Funding acquisition (equal), Project administration, Writing - original draft (supporting), Writing - review & editing (equal)

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First published: 09 May 2023
Edited by: Emily Frieben, Managing Editor and David Scott, Review Editor

Abstract

Differential privacy (DP) is in our smart phones, web browsers, social media, and the federal statistics used to allocate billions of dollars. Despite the mathematical concept being only 17 years old, differential privacy has amassed a rapidly growing list of real-world applications, such as Meta and US Census Bureau data. Why is DP so pervasive? DP is currently the only mathematical framework that provides a finite and quantifiable bound on disclosure risk when releasing information from confidential data. Previous concepts of data privacy and confidentiality required various assumptions about how a bad actor might attack sensitive data. DP is often called formally private because statisticians can mathematically prove the worst-case scenario privacy loss that could result from releasing information based on the confidential data. Although DP ushered in a new era of data privacy and confidentiality methodologies, many researchers and data practitioners criticize differentially private frameworks. In this paper, we provide readers a critical overview of the current state-of-the-art research on formal privacy methodologies and various relevant perspectives, challenges, and opportunities.

This article is categorized under:

  • Applications of Computational Statistics > Defense and National Security

Graphical Abstract

Side profile of a woman with a digitalized outline of her face a few inches infront of her against a blue background.

OPEN RESEARCH BADGES

Open Data

This article has earned an Open Data badge for making publicly available the digitally-shareable data necessary to reproduce the reported results.

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no new data were created or analyzed in this study.