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.

Getting started

Package details

AuthorStaal A. Vinterbo <>
MaintainerStaal A. Vinterbo <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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PrivateLR documentation built on May 2, 2019, 2:10 p.m.