DPpack: An R Package for Differentially Private Statistical Analysis and
  Machine Learning
Spencer Giddens Fang Liu
Differential privacy (DP) is the state-of-the-art framework for guaranteeing
privacy for individuals when releasing aggregated statistics or building
statistical/machine learning models from data. We develop the open-source R
package DPpack that provides a large toolkit of differentially private
analysis. The current version of DPpack implements three popular mechanisms for
ensuring DP: Laplace, Gaussian, and exponential. Beyond that, DPpack provides a
large toolkit of easily accessible privacy-preserving descriptive statistics
functions. These include mean, variance, covariance, and quantiles, as well as
histograms and contingency tables. Finally, DPpack provides user-friendly
implementation of privacy-preserving versions of logistic regression, SVM, and
linear regression, as well as differentially private hyperparameter tuning for
each of these models. This extensive collection of implemented differentially
private statistics and models permits hassle-free utilization of differential
privacy principles in commonly performed statistical analysis. We plan to
continue developing DPpack and make it more comprehensive by including more
differentially private machine learning techniques, statistical modeling and
inference in the future.