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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "diffpriv" in publications use:'
type: software
license: MIT
title: 'diffpriv: Easy Differential Privacy'
version: 0.4.2
identifiers:
- type: doi
  value: 10.32614/CRAN.package.diffpriv
abstract: 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) <https://doi.org/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.
authors:
- family-names: Rubinstein
  given-names: Benjamin
  email: brubinstein@unimelb.edu.au
- family-names: Aldà
  given-names: Francesco
  email: francesco.alda@gmail.com
preferred-citation:
  type: conference-paper
  title: Pain-Free Random Differential Privacy with Sensitivity Sampling
  authors:
  - family-names: Rubinstein
    given-names: Benjamin I. P.
  - family-names: Alda
    given-names: Francesco
  year: '2017'
  collection-title: 34th International Conference on Machine Learning (ICML'2017)
  collection-type: proceedings
  conference:
    name: 34th International Conference on Machine Learning (ICML'2017)
repository: https://cranhaven.r-universe.dev
repository-code: https://github.com/brubinstein/diffpriv
commit: c66e0c12ff6e807bd6c154f6834ce23d57d791ac
url: http://brubinstein.github.io/diffpriv
date-released: '2017-07-16'
contact:
- family-names: Rubinstein
  given-names: Benjamin
  email: brubinstein@unimelb.edu.au