DeepAI AI Chat

A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries

05/12/2021

∙

by Wei Dong, et al.

∙

∙

Releasing the result size of conjunctive queries and graph pattern queries under differential privacy (DP) has received considerable attention in the literature, but existing solutions do not offer any optimality guarantees. We provide the first DP mechanism for this problem with a fairly strong notion of optimality, which can be considered as a natural relaxation of instance-optimality.

Wei Dong
38 publications
Ke Yi
13 publications

page 1

page 2

page 3

page 4

∙ 07/19/2022

Composition Theorems for Interactive Differential Privacy

An interactive mechanism is an algorithm that stores a data set and answ...

0 Xin Lyu, et al. ∙

∙ 02/09/2023

Pushing the Boundaries of Private, Large-Scale Query Answering

We address the problem of efficiently and effectively answering large nu...

0 Brendan Avent, et al. ∙

∙ 03/01/2023

Subset-Based Instance Optimality in Private Estimation

We propose a new definition of instance optimality for differentially pr...

0 Travis Dick, et al. ∙

∙ 02/09/2021

Differential Privacy for Binary Functions via Randomized Graph Colorings

We present a framework for designing differentially private (DP) mechani...

0 Rafael G. L. D'Oliveira, et al. ∙

∙ 09/25/2019

Differential Privacy for Evolving Almost-Periodic Datasets with Continual Linear Queries: Application to Energy Data Privacy

For evolving datasets with continual reports, the composition rule for d...

0 Farhad Farokhi, et al. ∙

∙ 07/29/2021

HTF: Homogeneous Tree Framework for Differentially-Private Release of Location Data

Mobile apps that use location data are pervasive, spanning domains such ...

0 Sina Shaham, et al. ∙

∙ 05/15/2018

How Private Is Your Voting? A Framework for Comparing the Privacy of Voting Mechanisms

Voting privacy has received a lot of attention across several research c...

0 Ao Liu, et al. ∙