diffpriv: Easy Differential Privacy (original) (raw)
An implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms include the Laplace mechanism for releasing numeric aggregates, and the exponential mechanism for releasing set elements. A sensitivity sampler (Rubinstein & Alda, 2017) <doi:10.48550/arXiv.1706.02562> permits sampling target non-private function sensitivity; combined with the generic mechanisms, it permits turn-key privatization of arbitrary programs.
| Version: | 0.4.2 |
|---|---|
| Depends: | R (≥ 3.4.0) |
| Imports: | gsl, methods, stats |
| Suggests: | randomNames, testthat, knitr, rmarkdown |
| Published: | 2017-07-18 |
| DOI: | 10.32614/CRAN.package.diffpriv |
| Author: | Benjamin Rubinstein [aut, cre], Francesco Aldà [aut] |
| Maintainer: | Benjamin Rubinstein |
| BugReports: | https://github.com/brubinstein/diffpriv/issues |
| License: | MIT + file |
| URL: | https://github.com/brubinstein/diffpriv,http://brubinstein.github.io/diffpriv |
| NeedsCompilation: | no |
| Citation: | diffpriv citation info |
| Materials: | README, NEWS |
| In views: | OfficialStatistics |
| CRAN checks: | diffpriv results |
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