Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the Gaussian Mechanism for Differential Privacy: Analytical
  Calibration and Optimal Denoising   The Gaussian mechanism is an essential building block used in multitude of
differentially private data analysis algorithms. In this paper we revisit the
Gaussian mechanism and show that the original analysis has several important
limitations. Our analysis reveals that the variance formula for the original
mechanism is far from tight in the high privacy regime ($\varepsilon \to 0$)
and it cannot be extended to the low privacy regime ($\varepsilon \to \infty$).
We address these limitations by developing an optimal Gaussian mechanism whose
variance is calibrated directly using the Gaussian cumulative density function
instead of a tail bound approximation. We also propose to equip the Gaussian
mechanism with a post-processing step based on adaptive estimation techniques
by leveraging that the distribution of the perturbation is known. Our
experiments show that analytical calibration removes at least a third of the
variance of the noise compared to the classical Gaussian mechanism, and that
denoising dramatically improves the accuracy of the Gaussian mechanism in the
high-dimensional regime.

24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ