Project details for diffpriv

Logo diffpriv 0.4.2

by brubinstein - July 18, 2017, 16:09:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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The diffpriv package makes privacy-aware data science in R easy. diffpriv implements the formal framework of differential privacy: differentially-private mechanisms can safely release to untrusted third parties: statistics computed, models fit, or arbitrary structures derived on privacy-sensitive data. Due to the worst-case nature of the framework, mechanism development typically requires involved theoretical analysis. diffpriv offers a turn-key approach to differential privacy by automating this process with sensitivity sampling in place of theoretical sensitivity analysis. This sensitivity sampler operates with any of a number of common generic mechanisms including the Laplace, Gaussian (numeric release), exponential (private optimization) and Bernstein (function release) mechanisms.

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Agnostic
Tags: R, Differential Privacy, Privacy
Archive: download here


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