Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
2026-07-20 • Information Theory
Information TheoryCryptography and Security
AI summaryⓘ
The authors study how to better protect privacy when creating fake data that looks like real data while still being useful. They show that using public data directly inside privacy tools, rather than just before or after, can help make these privacy tools smarter. They focus on a specific way to measure data called histograms and design a new method called PubMix that uses public data to improve privacy without losing too much accuracy. Their tests show PubMix works better than older methods that don't consider the public data during privacy protection.
Differential PrivacySynthetic DataPublic DataHistogram EstimatorsPrivacy MechanismsData SynthesisStatistical FidelityDomain-Aware Mechanisms
Authors
Sajani Vithana, Sangwon Jung, Haoyang Hu, Viveck R. Cadambe, Flavio P. Calmon, Haewon Jeong
Abstract
Differential privacy (DP) imposes fundamental trade-offs between privacy and statistical fidelity in synthetic data generation. While access to public data has been shown to improve these trade-offs empirically, existing approaches use public data only indirectly, through pre-processing (e.g., using pre-trained generative models) or post-processing steps (e.g., matching target statistics estimated from public datasets), while relying on domain-agnostic DP mechanisms. In this work, we lay the theoretical framework to study the principled incorporation of public data into DP mechanisms themselves. We consider normalized histograms as distribution estimators and characterize the asymptotically optimal domain-aware privacy mechanism within a specific class of DP mechanisms. We introduce PubMix, a public-data-aware DP mechanism that can be used in histogram-based data synthesis pipelines. Our experiments demonstrate that PubMix significantly improves synthetic data generation quality compared to domain-agnostic privacy mechanisms.