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 <firstname.lastname@example.org>|
|Date of publication||2014-10-31 16:16:00|
|Maintainer||Staal A. Vinterbo <email@example.com>|
|License||GPL (>= 2)|
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