Graphical-model based estimation and inference for differential privacy
arXiv:1901.09136
Abstract
Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new queries. In this work, we provide an approach to solve this estimation problem efficiently using graphical models, which is particularly effective when the distribution is high-dimensional but the measurements are over low-dimensional marginals. We show that our approach is far more efficient than existing estimation techniques from the privacy literature and that it can improve the accuracy and scalability of many state-of-the-art mechanisms.
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- Synthetic Tabular Data: Methods, Attacks and Defenses
- Differentially Private Data Generation with Missing Data
- Statistical Inference in the Differential Privacy Model
- Differentially Private Quantiles
- Relaxed Marginal Consistency for Differentially Private Query Answering
- Prior-Aware Distribution Estimation for Differential Privacy
- Privacy-Enhanced Database Synthesis for Benchmark Publishing (Technical Report)
- DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges