AI RESEARCH
Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
arXiv CS.LG
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ArXi:2506.03467v3 Announce Type: replace-cross Gaussian Mixture Models (GMMs) are widely used statistical models for representing multi-modal data distributions, with numerous applications in data mining, pattern recognition, data simulation, and machine learning. However, recent research has shown that releasing GMM parameters poses significant privacy risks, potentially exposing sensitive information about the underlying data. In this paper, we address the challenge of releasing GMM parameters while ensuring differential privacy (DP) guarantees.