Package: PrivateLR 1.2-22

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.

Authors:Staal A. Vinterbo <[email protected]>

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12 exports 0.00 score 0 dependencies 1 scripts 148 downloads

Last updated 7 years agofrom:186f6945da. Checks:OK: 7. Indexed: yes.

Exports:dplrdplr.data.framedplr.factordplr.formuladplr.logicaldplr.matrixdplr.numericpredict.dplrprint.dplrprint.summary.dplrscaledsummary.dplr

Dependencies: