PrivateLR: Differentially Private Regularized Logistic Regression

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 <doi:10.1007/11681878_14>), 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 record, any measurable set S, and the randomness is taken over the choices F makes.

Version:	1.2-22
Published:	2018-03-20
Author:	Staal A. Vinterbo
Maintainer:	Staal A. Vinterbo <Staal.Vinterbo at ntnu.no>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	PrivateLR results

Documentation:

Reference manual:

PrivateLR.pdf

Downloads:

Package source:	PrivateLR_1.2-22.tar.gz
Windows binaries:	r-devel: PrivateLR_1.2-22.zip, r-release: PrivateLR_1.2-22.zip, r-oldrel: PrivateLR_1.2-22.zip
macOS binaries:	r-release (arm64): PrivateLR_1.2-22.tgz, r-oldrel (arm64): PrivateLR_1.2-22.tgz, r-release (x86_64): PrivateLR_1.2-22.tgz, r-oldrel (x86_64): PrivateLR_1.2-22.tgz
Old sources:	PrivateLR archive

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