Making valid statistical inferences from privatized data is a key challenge in modern analysis. In Bayesian settings, data augmentation MCMC (DAMCMC) methods impute unobserved confidential data given noisy privatized summaries, enabling principled …
Many modern statistical analysis and machine learning applications require training models on sensitive user data. Differential privacy provides a formal guarantee that individual-level information about users does not leak. In this framework, …
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 requires privacy protection. We show that the distance between the sampling …