DP-EM: Differentially Private Expectation Maximization

Privacy Without Regret: Differentially Private Inference-Time Alignment

Private Inference-Time Pessimism (PrivITP) is introduced, which combines $\chi^2$-regularized rejection sampling with a two-phase Gaussian mechanism, and achieves ex-post $(\epsilon,\delta)$-DP with a privacy cost independent of the number of responses, cleanly decouples the regularization parameter from the privacy pa...

Statistical Properties of Nonparametric MLE under Laplace Noise

This work studies the problem of estimating the distribution of the latent confidential data from the privatized observations via the nonparametric maximum likelihood estimator (NPMLE) under an i.i.d. sampling model, and shows that the NPMLE remains consistent when the Laplace noise grows at a rate slower than $n^{3/16...

Privacy Amplification Without Independence: How Far Negative Dependence Carries the Guarantees of Poisson Subsampling

Together, these results give a substitution map for privacy accounting: when Poisson-based computations remain sound for structured participation, where they fail, and what sound alternatives cost in deployment.

On the Computational and Statistical Efficiency of the Empirical Maximum Entropy on the Mean Method

It is shown that the MEM dual problem admits a reformulation as an expected risk minimization problem, thereby placing MEM within the modern framework of stochastic optimization and enabling scalable stochastic gradient algorithms for large-scale inverse problems.

Optimizing DP Gaussian Mean Estimation in R

Bounds on the Posterior-to-Prior Ratios for Inclusion Belief under Bounded Differential Privacy

The findings suggest that the inferential privacy guarantees provided by differentially private mechanisms may be substantially stronger in practice than what is implied by the theoretical upper limit.