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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.
| Version: | 1.2-22 |
| Published: | 2018-03-20 |
| Author: | Staal A. Vinterbo |
| Maintainer: | Staal A. Vinterbo <Staal.Vinterbo at ntnu.no> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | PrivateLR results |
| Reference manual: | PrivateLR.pdf |
| Package source: | PrivateLR_1.2-22.tar.gz |
| Windows binaries: | r-devel: PrivateLR_1.2-22.zip, r-release: PrivateLR_1.2-22.zip, r-oldrel: PrivateLR_1.2-22.zip |
| macOS binaries: | r-release (arm64): PrivateLR_1.2-22.tgz, r-oldrel (arm64): PrivateLR_1.2-22.tgz, r-release (x86_64): PrivateLR_1.2-22.tgz, r-oldrel (x86_64): PrivateLR_1.2-22.tgz |
| Old sources: | PrivateLR archive |
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