Wolfram Wiesemann
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The paper introduces 'mixture mechanisms,' a novel class of additive noise mechanisms that achieve differential privacy for real-valued queries, significantly reducing noise compared to the standard Gaussian mechanism, especially in low-privacy settings.
The paper introduces 'mixture mechanisms,' a novel class of additive noise mechanisms that achieve approximate differential privacy by mixing multiple Gaussian distributions, resulting in lower noise and improved performance compared to the standard analytic Gaussian mechanism.
Papers
Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
The paper introduces 'mixture mechanisms,' a novel class of additive noise mechanisms that achieve differential privacy for real-valued queries, significantly reducing noise compared to the standard G…