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  "Title": "Easy Differential Privacy",
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  "Date": "2017-07-18",
  "Authors@R": "c(\nperson(\"Benjamin\", \"Rubinstein\", email = \"brubinstein@unimelb.edu.au\", role = c(\"aut\", \"cre\")),\nperson(\"Francesco\", \"Aldà\", email = \"francesco.alda@gmail.com\", role = \"aut\"))",
  "Description": "An implementation of major general-purpose mechanisms for\nprivatizing statistics, models, and machine learners, within\nthe framework of differential privacy of Dwork et al. (2006)\n<doi:10.1007/11681878_14>. Example mechanisms include the\nLaplace mechanism for releasing numeric aggregates, and the\nexponential mechanism for releasing set elements. A sensitivity\nsampler (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits\nsampling target non-private function sensitivity; combined with\nthe generic mechanisms, it permits turn-key privatization of\narbitrary programs.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/brubinstein/diffpriv,\nhttp://brubinstein.github.io/diffpriv",
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  "Date/Publication": "2022-07-01 05:30:18 UTC",
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