Tight End-to-End Accounting for Differentially Private Synthetic Data
Differentially private (DP) pipelines often combine heterogeneous mechanisms, yet their guarantees are best analyzed using different notions of privacy (e.g., bounded range, zCDP, ADP). When composing them, we have to analyze the guarantees through a reduced common representation that loses accounting precision. This issue is particularly relevant in DP synthetic data generation, where data preprocessing, generation algorithms, and DP-SGD could appear within the same pipeline. We develop novel tools for tight end-to-end accounting of such compositions using privacy loss distributions (PLD), $f$-DP, and Gaussian DP (GDP). In particular, we derive PLD and GDP analyses of bounded-range mecha...
| Authors | Georgi Ganev, Vadym Doroshenko, Arun Ganesh et al. |
|---|---|
| Primary category | cs.CR |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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