Cart (Loading....) | Create Account
Close category search window

Compressed and Privacy-Sensitive Sparse Regression

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shuheng Zhou ; ETH Zurich, Zurich ; Lafferty, J. ; Wasserman, L.

Recent research has studied the role of sparsity in high-dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models. This line of work shows that lscr1 -regularized least squares regression can accurately estimate a sparse linear model from noisy examples in high dimensions. We study a variant of this problem where the original n input variables are compressed by a random linear transformation to m Lt n examples in p dimensions, and establish conditions under which a sparse linear model can be successfully recovered from the compressed data. A primary motivation for this compression procedure is to anonymize the data and preserve privacy by revealing little information about the original data. We characterize the number of projections that are required for lscr1 -regularized compressed regression to identify the nonzero coefficients in the true model with probability approaching one, a property called ldquosparsistence.rdquo We also show that lscr1 -regularized compressed regression asymptotically predicts as well as an oracle linear model, a property called ldquopersistence.rdquo Finally, we characterize the privacy properties of the compression procedure, establishing upper bounds on the mutual information between the compressed and uncompressed data that decay to zero.

Published in:

Information Theory, IEEE Transactions on  (Volume:55 ,  Issue: 2 )

Date of Publication:

Feb. 2009

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.