The problem of sampling exchangeable random variables arises in many Bayesian inference tasks, especially in data imputation given a privatized summary statistics. These permutation-invariant joint distributions often have dependency structures 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 requires privacy protection. We show that the distance between the sampling …
We consider a vector of $N$ independent binary variables, each with a different probability of success. The distribution of the vector conditional on its sum is known as the conditional Bernoulli distribution. Assuming that $N$ goes to infinity and …