Differentially Private Statistical Analysis and Machine Learning
An implementation of common statistical analysis and models with
differential privacy (Dwork et al., 2006a)
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)
, Gaussian mechanism (Dwork et al., 2006b)
, analytic Gaussian mechanism (Balle & Wang, 2018)
< https://proceedings.mlr.press/v80/balle18a.html>, and exponential mechanism
(McSherry & Talwar, 2007) .