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.

AuthorStaal A. Vinterbo <sav@ucsd.edu>
Date of publication2014-10-31 16:16:00
MaintainerStaal A. Vinterbo <sav@ucsd.edu>
LicenseGPL (>= 2)
Version1.2-21

View on CRAN

Functions

dplr Man page
dplr.data.frame Man page
dplr.factor Man page
dplr.formula Man page
dplr.logical Man page
dplr.matrix Man page
dplr.numeric Man page
predict.dplr Man page
print.dplr Man page
print.summary.dplr Man page
PrivateLR Man page
scaled Man page
summary.dplr Man page

Files

PrivateLR
PrivateLR/NAMESPACE
PrivateLR/R
PrivateLR/R/dplr.r
PrivateLR/MD5
PrivateLR/DESCRIPTION
PrivateLR/man
PrivateLR/man/dplr.Rd

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