New framework unifies privacy analysis for Bayesian posterior sampling

Researchers have developed a unified framework using Rényi divergence to analyze the inherent privacy guarantees of Bayesian posterior sampling methods. This framework quanties the privacy loss from One-Posterior-Sampling (OPS) by examining posterior exponential moments of the single-record log-likelihood ratio. The study establishes explicit differential privacy (DP) guarantees under various tail regimes and applies the framework to diverse Bayesian models, including linear regression and generalized linear models, demonstrating how factors like sample size and model structure influence inherent DP guarantees. AI

IMPACT Provides a theoretical foundation for understanding and improving privacy in Bayesian machine learning models.

RANK_REASON Academic paper detailing a new theoretical framework for privacy analysis in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Bayesian Models of Cognition
• categorical likelihoods
• differential privacy
• Gaussian-Gaussian conjugate pair
• Gaussian Likelihood Geometry of Projective Varieties
• Gaussian priors
• Generalized Linear Models
• linear regression
• One-Posterior-Sampling
• Rényi Divergence

• paper
• safety

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