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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.
Getting started
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Package details |
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| Author | Staal A. Vinterbo <Staal.Vinterbo@ntnu.no> |
| Maintainer | Staal A. Vinterbo <Staal.Vinterbo@ntnu.no> |
| License | GPL (>= 2) |
| Version | 1.2-22 |
| Package repository | View on CRAN |
| Installation |
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