Description:

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 mloss.org.

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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