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Found 7 papers, 4 papers with code
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
This technique allows for public data to be included in a graphical-model-based mechanism.
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
We propose a simplified setting that distills key facets of these methods and isolates the impact of linearly correlated noise.
Relaxed Marginal Consistency for Differentially Private Query Answering
Private-PGM is a recent approach that uses graphical models to represent the data distribution, with complexity proportional to that of exact marginal inference in a graphical model with structure determined by the co-occurrence of variables in the noisy measurements.
Fair Decision Making using Privacy-Protected Data
Data collected about individuals is regularly used to make decisions that impact those same individuals.
Databases
Graphical-model based estimation and inference for differential privacy
Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements.
Differentially Private Learning of Graphical Models using CGMs
A naive learning algorithm that uses the noisy sufficient statistics “as is” outperforms general-purpose differentially private learning algorithms.
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models
We investigate the problem of learning discrete, undirected graphical models in a differentially private way.
