Evaluating Differentially Private Generation of Domain-Specific Text

What is it about?

It’s about a unified benchmark for differentially private text generation. The work evaluates how well Differential Privacy (DP)-based generative methods preserve utility and fidelity on domain-specific datasets (e.g., healthcare, finance) under realistic privacy budgets and pre-training settings. Testing state-of-the-art methods across five datasets, it finds substantial performance drop versus real data, especially under strict DP, highlighting current limitations and the need for better privacy-preserving data sharing and standardized, realistic evaluation.

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Photo by BoliviaInteligente on Unsplash

Photo by BoliviaInteligente on Unsplash

Why is it important?

Real-world data in high-stakes domains is hard to share due to privacy and regulation; DP synthetic data is a potential workaround. There’s no standard, realistic way to evaluate DP text generation today; this benchmark fills that gap. It uses practical settings (realistic ε, pre-training effects, multiple metrics, domain-specific data), making results actionable. Findings show sizable utility/fidelity loss under strict DP, clarifying current limits and preventing overclaiming. It sets a baseline for fair comparison, guiding research toward more effective, privacy-preserving data sharing methods.