Local differential privacy (LDP) protects individuals in a dataset by perturbing each measurement before release. For real-valued data, a widely used mechanism is additive Laplace …
Making valid statistical inferences from privatized data is a key challenge in modern analysis. In Bayesian settings, data augmentation MCMC (DAMCMC) methods impute unobserved …
Many modern statistical analysis and machine learning applications require training models on sensitive user data. Differential privacy provides a formal guarantee that …
We develop both theory and algorithms to analyze privatized data in the unbounded differential privacy(DP), where even the sample size is considered a sensitive quantity that …
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform …
