PrivateLR: Differentially Private Regularized Logistic Regression

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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.

Author
Staal A. Vinterbo <sav@ucsd.edu>
Date of publication
2014-10-31 16:16:00
Maintainer
Staal A. Vinterbo <sav@ucsd.edu>
License
GPL (>= 2)
Version
1.2-21

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Man pages

dplr
Differentially Private Logistic Regression

Files in this package

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