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 |

PrivateLR

PrivateLR/NAMESPACE

PrivateLR/R

PrivateLR/R/dplr.r

PrivateLR/MD5

PrivateLR/DESCRIPTION

PrivateLR/man

PrivateLR/man/dplr.Rd
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