Differentially Private Statistical Analysis and Machine Learning
Calibrate Analytic Gaussian Mechanism
Differentially Private Covariance Data Access Function
Differentially Private Covariance
Privacy-preserving Empirical Risk Minimization for Binary Classificati...
Privacy-preserving Empirical Risk Minimization for Regression
Exponential Mechanism
Gaussian Mechanism
Generator for Huber Loss Function Gradient
Generator for Huber Loss Function
Generator for Sampling Distribution Function for Gaussian Kernel
Differentially Private Histogram Data Access Function
Differentially Private Histogram
Laplace Mechanism
Privacy-preserving Linear Regression
Privacy-preserving Logistic Regression
Cross Entropy Loss Function
Cross Entropy Loss Function Gradient
Squared error Loss Function Gradient
Squared Error Loss Function
Linear Map Function Gradient
Sigmoid Map Function Gradient
Linear Map Function
Sigmoid Map Function
Differentially Private Mean Data Access Function
Differentially Private Mean
Differentially Private Median
Transform Function for Gaussian Kernel Approximation
Differentially Private Pooled Covariance Data Access Function
Differentially Private Pooled Covariance
Differentially Private Pooled Variance Data Access Function
Differentially Private Pooled Variance
Differentially Private Quantile Data Access Function
Differentially Private Quantile
l2 Regularizer Gradient
l2 Regularizer
Differentially Private Standard Deviation
Privacy-preserving Support Vector Machine
Differentially Private Contingency Table Data Access Function
Differentially Private Contingency Table
Privacy-preserving Hyperparameter Tuning for Binary Classification Mod...
Privacy-preserving Hyperparameter Tuning for Linear Regression Models
Differentially Private Variance Data Access Function
Differentially Private Variance
Privacy-preserving Weighted Empirical Risk Minimization
An implementation of common statistical analysis and models with differential privacy (Dwork et al., 2006a) <doi:10.1007/11681878_14> guarantees. The package contains, for example, functions providing differentially private computations of mean, variance, median, histograms, and contingency tables. It also implements some statistical models and machine learning algorithms such as linear regression (Kifer et al., 2012) <https://proceedings.mlr.press/v23/kifer12.html> and SVM (Chaudhuri et al., 2011) <https://jmlr.org/papers/v12/chaudhuri11a.html>. In addition, it implements some popular randomization mechanisms, including the Laplace mechanism (Dwork et al., 2006a) <doi:10.1007/11681878_14>, Gaussian mechanism (Dwork et al., 2006b) <doi:10.1007/11761679_29>, analytic Gaussian mechanism (Balle & Wang, 2018) <https://proceedings.mlr.press/v80/balle18a.html>, and exponential mechanism (McSherry & Talwar, 2007) <doi:10.1109/FOCS.2007.66>.