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

• URL: http://arxiv.org/abs/2412.13030v1
• Date: Tue, 17 Dec 2024 15:50:14 GMT
• Title: Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives
• Authors: Lucas Rosenblatt, Bill Howe, Julia Stoyanovich,
• Abstract summary: In the United States, differential privacy (DP) is the dominant technical operationalization of privacy-preserving data analysis.<n>This study qualitatively examines one class of DP mechanisms: private data synthesizers.
• Score: 14.736115103446101
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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