Private Posterior Inference Consistent with Public Information: A Case Study in Small Area Estimation from Synthetic Census Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Methods for generating differentially-private (DP) synthetic data have received recent attention as large government agencies such as the U.S. Census have decided to release DP synthetic data for public usage. In the synthetic data generation process, it is common to post-process the privatized results so that the final synthetic data agrees with what the data curator considers public information. Our contributions are three fold: 1) we show empirically that using post-processing to incorporate public information in contingency tables can lead to sub-optimal inference, 2) we propose an alternative Bayesian sampling framework that directly incorporates both noise due to DP and public information constraints, leading to improved inference, and 3) we demonstrate the proposed methodology on a study of the relationship between mortality rate and race in small areas given priviatized data from the CDC and U.S. Census.

Original languageEnglish (US)
Title of host publicationPrivacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2020, Proceedings
EditorsJosep Domingo-Ferrer, Krishnamurty Muralidhar
PublisherSpringer Science and Business Media Deutschland GmbH
Pages323-336
Number of pages14
ISBN (Print)9783030575205
DOIs
StatePublished - 2020
EventInternational Conference on Privacy in Statistical Databases, PSD 2020 - Tarragona, Spain
Duration: Sep 23 2020Sep 25 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12276 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Privacy in Statistical Databases, PSD 2020
Country/TerritorySpain
CityTarragona
Period9/23/209/25/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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