DeepAI AI Chat
Log In Sign Up

Differentially Private Hierarchical Group Size Estimation

by   Yu-Hsuan Kuo, et al.
Colgate University
Duke University
Penn State University

Consider the problem of estimating, for every integer j, the number of households with j people in them, while protecting the privacy of individuals. Add in a geographical component, so that the household size distribution can be compared at the national, state, and county levels. This is an instance of the private hierarchical group size estimation problem, in which each group is associated with a size and a hierarchical attribute. In this paper, we introduce this problem, along with appropriate error metrics and propose a differentially private solution that generates group size estimates that are consistent across all levels of the hierarchy.


page 11

page 12


Differentially Private Hierarchical Count-of-Counts Histograms

We consider the problem of privately releasing a class of queries that w...

Reasoning in a Hierarchical System with Missing Group Size Information

The paper analyzes the problem of judgments or preferences subsequent to...

Differentially Private Mechanisms for Count Queries

In this paper, we consider the problem of responding to a count query (o...

Differentially Private Linear Regression over Fully Decentralized Datasets

This paper presents a differentially private algorithm for linear regres...

Differentially Private Health Tokens for Estimating COVID-19 Risk

In the fight against Covid-19, many governments and businesses are in th...

Differentially private depth functions and their associated medians

In this paper, we investigate the differentially private estimation of d...

Private Matrix Approximation and Geometry of Unitary Orbits

Consider the following optimization problem: Given n × n matrices A and ...