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Found 7 papers, 2 papers with code
Differentially Private Bayesian Linear Regression
Linear regression is an important tool across many fields that work with sensitive human-sourced data.
Differentially Private Bayesian Inference for Exponential Families
The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources.
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.
Consistently Estimating Markov Chains with Noisy Aggregate Data
We develop a new, simpler method of moments estimator that bypasses this problem and is consistent under noisy observations.
Bayesian Discovery of Threat Networks
A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven.
Network Detection Theory and Performance
The specific problem of network discovery is addressed as a special case of graph partitioning in which membership in a small subgraph of interest must be determined.
