An application of differentially private linear mixed modeling

John M. Abowd*, Matthew John Schneider

*Corresponding author for this work

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

2 Scopus citations

Abstract

We consider a differentially private MLE for the linear mixed-effects model with normal random errors. This model is important because it is frequently used in small area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying data. The differentially private estimator performs well compared to the regular MLE, and deteriorates as the protection increases, for a problem in which small-area variation is at the county level. More dimensions of random effects are needed to adequately represent the time-dimension of the data, and for these cases the differentially private MLE cannot be computed.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages614-619
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 11 2011

Other

Other11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/11/11

Keywords

  • Differential privacy
  • EBLUP
  • Linear mixed models
  • MLE
  • Privacy-preserving datamining
  • Quarterly workforce indicators
  • REML
  • Statistical disclosure limitation

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'An application of differentially private linear mixed modeling'. Together they form a unique fingerprint.

Cite this