Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives

TL;DR

This study explores data experts' perspectives on differential privacy in synthetic data, highlighting communication challenges, the need for context-aware benchmarks, and proposing standards and tiered access models to improve adoption.

Contribution

It provides qualitative insights into practitioner needs and challenges, and offers recommendations for improving differential privacy practices and standards in synthetic data.

Findings

Practitioners emphasize the need for context-aware DP solutions.

Communication challenges hinder understanding and adoption of DP.

Recommendations include improving benchmarks, publishing standards, and tiered data access models.

Abstract

Data privacy is a core tenet of responsible computing, and in the United States, differential privacy (DP) is the dominant technical operationalization of privacy-preserving data analysis. With this study, we qualitatively examine one class of DP mechanisms: private data synthesizers. To that end, we conducted semi-structured interviews with data experts: academics and practitioners who regularly work with data. Broadly, our findings suggest that quantitative DP benchmarks must be grounded in practitioner needs, while communication challenges persist. Participants expressed a need for context-aware DP solutions, focusing on parity between research outcomes on real and synthetic data. Our analysis led to three recommendations: (1) improve existing insufficient sanitized benchmarks; successful DP implementations require well-documented, partner-vetted use cases, (2) organizations using DP synthetic data should publish discipline-specific standards of evidence, and (3) tiered data access models could allow researchers to gradually access sensitive data based on demonstrated competence with high-privacy, low-fidelity synthetic data.