arXiv subjects

Aaron R. Williams

Publications and source records attributed to Aaron R. Williams.

4 recordsLinked to original sources

arXiv2026

tidysynthesis: a Meta-Package for Synthetic Data Generation

Synthetic data generation enables data curators to more easily share datasets that limits the potential for disclosive inferences about data subjects in confidential datasets. Generating synthetic data requires navigating numerous design choices; however, most existing open source software fails to provide common software infrastructure for making such design choices efficiently. In this paper, we introduce tidysynthesis, a meta-package for synthetic data generation that enables better interoperability between existing modeling frameworks and statistical data privacy methods. tidysynthesis allows users more flexibility to specify and iterate on synthetic data algorithms by providing a common syntax to easily create and modify synthetic data generation pipelines. We demonstrate the features and extensibility of tidysynthesis, as well as provide end-to-end examples for synthetic data generation using data from the American Community Survey

stat.CO↗

arXiv2024

But Can You Use It? Design Recommendations for Differentially Private Interactive Systems

Accessing data collected by federal statistical agencies is essential for public policy research and improving evidence-based decision making, such as evaluating the effectiveness of social programs, understanding demographic shifts, or addressing public health challenges. Differentially private interactive systems, or validation servers, can form a crucial part of the data-sharing infrastructure. They may allow researchers to query targeted statistics, providing flexible, efficient access to specific insights, reducing the need for broad data releases and supporting timely, focused research. However, they have not yet been practically implemented. While substantial theoretical work has been conducted on the privacy and accuracy guarantees of differentially private mechanisms, prior efforts have not considered usability as an explicit goal of interactive systems. This work outlines and considers the barriers to developing differentially private interactive systems for informing public policy and offers an alternative way forward. We propose balancing three design considerations: privacy assurance, statistical utility, and system usability, we develop recommendations for making differentially private interactive systems work in practice, we present an example architecture based on these recommendations, and we provide an outline of how to conduct the necessary user-testing. Our work seeks to move the practical development of differentially private interactive systems forward to better aid public policy making and spark future research.

cs.HC↗

arXiv2023

Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy

The authors discuss their experience applying differential privacy with a complex data set with the goal of enabling standard approaches to statistical data analysis. They highlight lessons learned and roadblocks encountered, distilling them into incompatibilities between current practices in statistical data analysis and differential privacy that go beyond issues which can be solved with a noisy measurements file. The authors discuss how overcoming these incompatibilities require compromise and a change in either our approach to statistical data analysis or differential privacy that should be addressed head-on.

cs.CR↗

arXiv2023

A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data

Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data is to allow individuals to query statistics without directly accessing the confidential data. This paper studies the feasibility of using differentially private (DP) methods to make certain queries while preserving privacy. We also include new methodological adaptations to existing DP regression methods for using new data types and returning standard error estimates. We define feasibility as the impact of DP methods on analyses for making public policy decisions and the queries accuracy according to several utility metrics. We evaluate the methods using Internal Revenue Service data and public-use Current Population Survey data and identify how specific data features might challenge some of these methods. Our findings show that DP methods are feasible for simple, univariate statistics but struggle to produce accurate regression estimates and confidence intervals. To the best of our knowledge, this is the first comprehensive statistical study of DP regression methodology on real, complex datasets, and the findings have significant implications for the direction of a growing research field and public policy.

stat.AP↗