Fundamental Limits of Database Alignment

05/10/2018
by   Daniel Cullina, et al.
0

We consider the problem of aligning a pair of databases with correlated entries. We introduce a new measure of correlation in a joint distribution that we call cycle mutual information. This measure has operational significance: it determines whether exact recovery of the correspondence between database entries is possible for any algorithm. Additionally, there is an efficient algorithm for database alignment that achieves this information theoretic threshold.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/04/2019

Database Alignment with Gaussian Features

We consider the problem of aligning a pair of databases with jointly Gau...
research
10/30/2020

Sharp threshold for alignment of graph databases with Gaussian weights

We study the fundamental limits for reconstruction in weighted graph (or...
research
07/05/2023

Gaussian Database Alignment and Gaussian Planted Matching

Database alignment is a variant of the graph alignment problem: Given a ...
research
01/23/2019

A Concentration of Measure Approach to Database De-anonymization

In this paper, matching of correlated high-dimensional databases is inve...
research
09/10/2018

Partial Recovery of Erdős-Rényi Graph Alignment via k-Core Alignment

We determine information theoretic conditions under which it is possible...
research
06/19/2012

Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information

The goal of temporal alignment is to establish time correspondence betwe...
research
02/01/2021

Attributed Graph Alignment

Motivated by various data science applications including de-anonymizing ...

Please sign up or login with your details

Forgot password? Click here to reset