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Implements two differentially private algorithms for estimating L2regularized logistic regression coefficients. A randomized algorithm F is epsilondifferentially 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.
Package details 


Author  Staal A. Vinterbo <[email protected]> 
Maintainer  Staal A. Vinterbo <[email protected]> 
License  GPL (>= 2) 
Version  1.222 
Package repository  View on CRAN 
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