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DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS

Abstract: 

In this paper, we propose a differentially-private canonical correlation analysis algorithm. Canonical correlation analysis (CCA) is often used in clustering applications for multi-view data. CCA finds subspaces for each view such that projecting each of the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. Differential-privacy is a framework for understanding the risk of inferring the data input to the algorithm based on the output. We investigate the performance of the proposed algorithm with varying privacy parameters and database parameters on synthetic and real data. Our results show that it is possible to have meaningful privacy with very good utility even for strict privacy guarantees.

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Paper Details

Authors:
Hafiz Imtiaz, Anand D. Sarwate
Submitted On:
9 November 2017 - 1:13pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Hafiz Imtiaz
Paper Code:
GlobalSIP1701
Document Year:
2017
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[1] Hafiz Imtiaz, Anand D. Sarwate, "DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2273. Accessed: Oct. 16, 2018.
@article{2273-17,
url = {http://sigport.org/2273},
author = {Hafiz Imtiaz; Anand D. Sarwate },
publisher = {IEEE SigPort},
title = {DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS},
year = {2017} }
TY - EJOUR
T1 - DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS
AU - Hafiz Imtiaz; Anand D. Sarwate
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2273
ER -
Hafiz Imtiaz, Anand D. Sarwate. (2017). DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS. IEEE SigPort. http://sigport.org/2273
Hafiz Imtiaz, Anand D. Sarwate, 2017. DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS. Available at: http://sigport.org/2273.
Hafiz Imtiaz, Anand D. Sarwate. (2017). "DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS." Web.
1. Hafiz Imtiaz, Anand D. Sarwate. DIFFERENTIALLY-PRIVATE CANONICAL CORRELATION ANALYSIS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2273