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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.

Versions across snapshots

VersionRepositoryFileSize
0.4.2 rolling linux/jammy R-4.5 diffpriv_0.4.2.tar.gz 709.3 KiB
0.4.2 rolling linux/noble R-4.5 diffpriv_0.4.2.tar.gz 709.2 KiB
0.4.2 rolling source/ R- diffpriv_0.4.2.tar.gz 518.4 KiB
0.4.2 latest linux/jammy R-4.5 diffpriv_0.4.2.tar.gz 709.3 KiB
0.4.2 latest linux/noble R-4.5 diffpriv_0.4.2.tar.gz 709.2 KiB
0.4.2 latest source/ R- diffpriv_0.4.2.tar.gz 518.4 KiB
0.4.2 2026-04-26 source/ R- diffpriv_0.4.2.tar.gz 518.4 KiB
0.4.2 2026-04-23 source/ R- diffpriv_0.4.2.tar.gz 518.4 KiB
0.4.2 2026-04-09 windows/windows R-4.5 diffpriv_0.4.2.zip 713.2 KiB
0.4.2 2025-04-20 source/ R- diffpriv_0.4.2.tar.gz 518.4 KiB

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