PrivateLR: Differentially Private Regularized Logistic Regression

PrivateLR implements two differentially private algorithms for estimating L2-regularized logistic regression coefficients. A randomized algorithm F is epsilon-differentially private (C. Dwork, Differential Privacy, ICALP 2006), if |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon for any pair D, D' of datasets that differ in exactly one element, any set S, and the randomness is taken over the choices F makes.

Version: 1.2-21
Published: 2014-10-31
Author: Staal A. Vinterbo
Maintainer: Staal A. Vinterbo <sav at ucsd.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: PrivateLR results

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Reference manual: PrivateLR.pdf
Package source: PrivateLR_1.2-21.tar.gz
Windows binaries: r-devel: PrivateLR_1.2-21.zip, r-release: PrivateLR_1.2-21.zip, r-oldrel: PrivateLR_1.2-21.zip
OS X El Capitan binaries: r-release: PrivateLR_1.2-21.tgz
OS X Mavericks binaries: r-oldrel: PrivateLR_1.2-21.tgz
Old sources: PrivateLR archive

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