Abstract
In this paper, we argue that synthetic data produced by Differentially Private generative models can be sufficiently anonymized and, therefore, anonymous data and regulatory compliant.
When Synthetic Data Met Regulation
Related papers
Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).
- Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective (2026)74% match
- Synthetic Data -- Anonymisation Groundhog Day (2020)72% match
- A Framework for Auditable Synthetic Data Generation (2022)72% match
- A Unified Framework for Quantifying Privacy Risk in Synthetic Data (2022)71% match
- Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance (2024)71% match
- The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets (2023)71% match
- Differentially Private Synthetic Data: Applied Evaluations and Enhancements (2020)70% match
- Privacy Amplification Through Synthetic Data: Insights from Linear Regression (2025)70% match