Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
The method addresses privacy risks when releasing Gaussian Mixture Model (GMM) parameters by ensuring differential privacy (DP). It uses Kullback-Leibler (KL) divergence as a utility metric to measure accuracy. The approach develops a framework that jointly designs noise perturbations and privacy budgets to minimize this divergence, balancing strong data protection with high model quality.