Covariance loss, Szemeredi regularity, and differential privacy

01/06/2023
by   March Boedihardjo, et al.
0

We show how randomized rounding based on Grothendieck's identity can be used to prove a nearly tight bound on the covariance loss–the amount of covariance that is lost by taking conditional expectation. This result yields a new type of weak Szemeredi regularity lemma for positive semidefinite matrices and kernels. Moreover, it can be used to construct differentially private synthetic data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/18/2017

Differentially Private Identity and Closeness Testing of Discrete Distributions

We investigate the problems of identity and closeness testing over a dis...
research
11/15/2021

Colored Noise Mechanism for Differentially Private Clustering

The goal of this paper is to propose and analyze a differentially privat...
research
05/03/2022

Optimal minimization of the covariance loss

Let X be a random vector valued in ℝ^m such that X_2≤ 1 almost surely. F...
research
05/28/2022

Differentially Private Covariance Revisited

In this paper, we present three new error bounds, in terms of the Froben...
research
01/18/2019

Differentially Private High Dimensional Sparse Covariance Matrix Estimation

In this paper, we study the problem of estimating the covariance matrix ...
research
07/13/2021

Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data

The protection of private information is of vital importance in data-dri...

Please sign up or login with your details

Forgot password? Click here to reset