diffprivhttp://mloss.orgUpdates and additions to diffprivenTue, 18 Jul 2017 16:09:59 -0000diffpriv 0.4.2<html><p>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. </p></html>Benjamin Rubinstein, Francesco AldaTue, 18 Jul 2017 16:09:59 -0000 privacyprivacy