May 16, 2018 · Abstract:The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms.
Our experiments show that analytical calibration re- moves at least a third of the variance of the noise compared to the classical Gaussian mechanism, and that ...
An optimal Gaussian mechanism is developed whose variance is calibrated directly using the Gaussian cumulative density function instead of a tail bound ...
Jun 7, 2018 · Abstract. The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms.
May 12, 2021 · Borja Balle, Yu-Xiang Wang: Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising.
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. International Conference on Machine Learning (ICML) ...
Nov 27, 2019 · This paper develops a class of mechanisms that enjoy non-asymptotic and unconditional optimality guarantees and forms the mechanism design problem as an ...
As first proposed by Balle and Wang in "Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising". Paper link ...
Aug 29, 2019 · The first improvement is an algorithmic noise calibration strategy that uses numerical evaluations of the Gaussian cumulative density function ( ...
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Nov 28, 2019 · Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently.