DPpack

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.

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