diffpriv: Easy Differential Privacy

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) <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
Author: Benjamin Rubinstein [aut, cre], Francesco Aldà [aut]
Maintainer: Benjamin Rubinstein <brubinstein at unimelb.edu.au>
BugReports: https://github.com/brubinstein/diffpriv/issues
License: MIT + file LICENSE
URL: https://github.com/brubinstein/diffpriv, http://brubinstein.github.io/diffpriv
NeedsCompilation: no
Citation: diffpriv citation info
Materials: README NEWS
CRAN checks: diffpriv results

Downloads:

Reference manual: diffpriv.pdf
Vignettes: bernstein
diffpriv
Package source: diffpriv_0.4.2.tar.gz
Windows binaries: r-devel: diffpriv_0.4.2.zip, r-devel-gcc8: diffpriv_0.4.2.zip, r-release: diffpriv_0.4.2.zip, r-oldrel: diffpriv_0.4.2.zip
OS X binaries: r-release: diffpriv_0.4.2.tgz, r-oldrel: diffpriv_0.4.2.tgz

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