diffpriv package is a collection of generic tools for privacy-aware
data science, under the formal framework of differential privacy. A
differentially-private mechanism can release responses to untrusted third
parties, models fit on privacy-sensitive data. Due to the formal worst-case
nature of the framework, however, mechanism development typically requires
diffpriv offers a turn-key approach to
Differential privacy's popularity is owed in part to a number of generic
mechanisms for privatizing non-private target functions. Virtual S4
DPMech-class captures common features of these mechanisms and
is superclass to:
DPMechLaplace: the Laplace mechanism of Dwork et al.
(2006) for releasing numeric vectors;
DPMechExponential: the exponential mechanism of McSherry
and Talwar (2007) for releasing solutions to optimizations, over numeric or
non-numeric sets; and
More mechanisms coming soon. Users can also develop new mechanisms by
DPMech-class-derived objects are initialized with a problem-specific
target function. Subsequently, the
releaseResponse method can privatize responses of
on input datasets. The level of corresponding privatization depends on given
DPParamsEps or derived parameters object.
diffpriv mechanisms have in common a reliance on the 'sensitivity' of
target function to small changes to input datasets. This sensitivity
must be provably bounded for an application's
target in order for
differential privacy to be proved, and is used to calibrate privacy-preserving
randomization. Unfortunately bounding sensitivity is often prohibitively
complex, for example if
target is an arbitrary computer program. All
DPMech-class mechanisms offer a
method due to Rubinstein and Ald<c3><a0> (2017) that repeatedly probes
to estimate sensitivity automatically. Mechanisms with estimated sensitivities
achieve a slightly weaker form of random differential privacy due to
Hall et al. (2013), but without any theoretical analysis necessary.
Benjamin I. P. Rubinstein and Francesco Ald<c3><a0>. "Pain-Free Random Differential Privacy with Sensitivity Sampling", accepted into the 34th International Conference on Machine Learning (ICML'2017), May 2017.
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. "Calibrating noise to sensitivity in private data analysis." In Theory of Cryptography Conference, pp. 265-284. Springer Berlin Heidelberg, 2006.
Frank McSherry and Kunal Talwar. "Mechanism design via differential privacy." In the 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), pp. 94-103. IEEE, 2007.
Rob Hall, Alessandro Rinaldo, and Larry Wasserman. "Random Differential Privacy." Journal of Privacy and Confidentiality, 4(2), pp. 43-59, 2012.
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## Not run: ## for full examples see the diffpriv vignette vignette("diffpriv") ## End(Not run)
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