PrivateLR: Differentially Private Regularized Logistic Regression
Version 1.2-21

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.

Getting started

Package details

AuthorStaal A. Vinterbo <>
Date of publication2014-10-31 16:16:00
MaintainerStaal A. Vinterbo <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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PrivateLR documentation built on May 29, 2017, 9:48 p.m.