DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning. (arXiv:2309.10965v1 [stat.ML])

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 …

analysis art current data differential privacy framework large machine machine learning machine learning models package privacy private state statistical analysis statistics toolkit version