Distributed Differentially-private Canonical Correlation Analysis

Hafiz Imtiaz, Anand D. Sarwate

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

1 Scopus citations

Abstract

We propose a distributed differentially-private canonical correlation analysis (CCA) algorithm to use on multi-view data. CCA finds a subspace for each view such that projecting the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. In applications involving privacy-sensitive data, such as medical imaging, distributed privacy-preserving algorithms can let data holders maintain local control of their data while participating in joint computations with other data holders. Differential privacy is a framework for quantifying the privacy risk in such settings. However, conventional distributed differentially-private algorithms introduce more noise to guarantee a given level of privacy compared to their centralized counterparts. Our differentially-private CCA employs a noise-reduction strategy to achieve the same utility level as CCA on centralized data. Experiments on synthetic and real data show the benefit of our approach over conventional methods.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3112-3116
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: May 12 2019May 17 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period5/12/195/17/19

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • canonical correlation analysis
  • clustering
  • differential privacy
  • distributed data
  • multi-view learning

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