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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

 

Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques produce and release synthetic individual-level data in the DP framework. One key challenge to develop DIPS methods is the preservation of the statistical utility of synthetic data, especially in high-dimensional settings. We propose a new DIPS approach, STatistical Election to Partition Sequentially (STEPS) that partitions data by attributes according to their importance ranks according to either a practical or statistical importance measure. STEPS aims to achieve better original information preservation for the attributes with higher importance ranks and produce thus more useful synthetic data overall. We present an algorithm to implement the STEPS procedure and employ the privacy budget composability to ensure the overall privacy cost is controlled at the pre-specified value. We apply the STEPS procedure to both simulated data and the 2000–2012 Current Population Survey youth voter data. The results suggest STEPS can better preserve the population-level information and the original information for some analyses compared to PrivBayes, a modified Uniform histogram approach, and the flat Laplace sanitizer.