Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives
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| Format: | Preprint |
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2024
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| _version_ | 1866910749721362432 |
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| author | Rosenblatt, Lucas Howe, Bill Stoyanovich, Julia |
| author_facet | Rosenblatt, Lucas Howe, Bill Stoyanovich, Julia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives Rosenblatt, Lucas Howe, Bill Stoyanovich, Julia Human-Computer Interaction Cryptography and Security Databases 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. |
| title | Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives |
| topic | Human-Computer Interaction Cryptography and Security Databases |
| url | https://arxiv.org/abs/2412.13030 |