Easy Differential Privacy

An implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of Dwork et al. (2006) . Example mechanisms include the Laplace mechanism for releasing numeric aggregates, and the exponential mechanism for releasing set elements. A sensitivity sampler (Rubinstein & Alda, 2017) permits sampling target non-private function sensitivity; combined with the generic mechanisms, it permits turn-key privatization of arbitrary programs.


packageversion CRAN_Status_Badge Travis Build Status Coverage Status license minimal R version


The diffpriv package makes privacy-aware data science in R easy. diffpriv implements the formal framework of differential privacy: differentially-private mechanisms can safely release to untrusted third parties: statistics computed, models fit, or arbitrary structures derived on privacy-sensitive data. Due to the worst-case nature of the framework, mechanism development typically requires involved theoretical analysis. diffpriv offers a turn-key approach to differential privacy by automating this process with sensitivity sampling in place of theoretical sensitivity analysis.


Obtaining diffpriv is easy. From within R:



A typical example in differential privacy is privately releasing a simple target function of privacy-sensitive input data X. Say the mean of numeric data:

## a target function we'd like to run on private data X, releasing the result
target <- function(X) mean(X)

First load the diffpriv package (installed as above) and construct a chosen differentially-private mechanism for privatizing target.

## target seeks to release a numeric, so we'll use the Laplace mechanism---a
## standard generic mechanism for privatizing numeric responses
mech <- DPMechLaplace(target = target)

To run mech on a dataset X we must first determine the sensitivity of target to small changes to input dataset. One avenue is to analytically bound sensitivity (on paper; see the vignette) and supply it via the sensitivity argument of mechanism construction: in this case not hard if we assume bounded data, but in general sensitivity can be very non-trivial to calculate manually. The other approach, which we follow in this example, is sensitivity sampling: repeated probing of target to estimate sensitivity automatically. We need only specify a distribution for generating random probe datasets; sensitivitySampler() takes care of the rest. The price we pay for this convenience is the weaker form of random differential privacy.

## set a dataset sampling distribution, then estimate target sensitivity with
## sufficient samples for subsequent mechanism responses to achieve random
## differential privacy with confidence 1-gamma
distr <- function(n) rnorm(n)
mech <- sensitivitySampler(mech, oracle = distr, n = 5, gamma = 0.1)
#> Sampling sensitivity with m=285 gamma=0.1 k=285
[email protected]    ## DPMech and subclasses are S4: slots accessed via @
#> [1] 0.8089517

With a sensitivity-calibrated mechanism in hand, we can release private responses on a dataset X, displayed alongside the non-private response for comparison:

X <- c(0.328,-1.444,-0.511,0.154,-2.062) # length is sensitivitySampler() n
r <- releaseResponse(mech, privacyParams = DPParamsEps(epsilon = 1), X = X)
cat("Private response r$response:   ", r$response,
  "\nNon-private response target(X):", target(X))
#> Private response r$response:    -1.119506 
#> Non-private response target(X): -0.707

Getting Started

The above example demonstrates the main components of diffpriv:

  • Virtual class DPMech for generic mechanisms that captures the non-private target and releases privatized responses from it. Current subclasses
    • DPMechLaplace, DPMechGaussian: the Laplace and Gaussian mechanisms for releasing numeric responses with additive noise;
    • DPMechExponential: the exponential mechanism for privately optimizing over finite sets (which need not be numeric); and
    • DPMechBernstein: the Bernstein mechanism for privately releasing multivariate real-valued functions. See the bernstein vignette for more.
  • Class DPParamsEps and subclasses for encapsulating privacy parameters.
  • sensitivitySampler() method of DPMech subclasses estimates target sensitivity necessary to run releaseResponse() of DPMech generic mechanisms. This provides an easy alternative to exact sensitivity bounds requiring mathematical analysis. The sampler repeatedly probes [email protected] to estimate sensitivity to data perturbation. Running mechanisms with obtained sensitivities yield random differential privacy.

Read the package vignette for more, or news for the latest release notes.

Citing the Package

diffpriv is an open-source package offered with a permissive MIT License. Please acknowledge use of diffpriv by citing the paper on the sensitivity sampler:

Other relevant references to cite depending on usage:

  • Differential privacy and the Laplace mechanism: 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.
  • The Gaussian mechanism: Cynthia Dwork and Aaron Roth. "The algorithmic foundations of differential privacy." Foundations and Trends in Theoretical Computer Science 9(3–4), pp. 211-407, 2014.
  • The exponential mechanism: 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.
  • The Bernstein mechanism: Francesco Aldà and Benjamin I. P. Rubinstein. "The Bernstein Mechanism: Function Release under Differential Privacy." In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'2017), pp. 1705-1711, 2017.
  • Random differential privacy: Rob Hall, Alessandro Rinaldo, and Larry Wasserman. "Random Differential Privacy." Journal of Privacy and Confidentiality, 4(2), pp. 43-59, 2012.


diffpriv 0.4.2

  • Second vignette bernstein on: Bernstein approximations and use of DPMechBernstein for private function release.
  • Minor edits to docs

diffpriv 0.4.1

  • Expanding test coverage of Bernstein mechanism and function approximation code.

diffpriv 0.4.0

  • Addition of S3 constructor and predict() generic implementation for fitting (non-iterated) Bernstein polynomial function approximations.
  • Addition of DPMechBernstein class implementing the Bernstein mechanism of Alda and Rubinstein (AAAI'2017), for privately releasing functions.
  • Bug fix in the Laplace random sampler affecting DPMechLaplace
  • Unit test coverage of new functionality; general documentation improvements.

diffpriv 0.3.2

  • Addition of DPMechGaussian class for the generic Gaussian mechanism to README, Vignette. Resolves #2
  • Minor test additions.

diffpriv 0.3.1

  • Refactoring around releaseResponse() method in DPMechNumeric. Resolves #1
  • Increased test coverage.

diffpriv 0.3.0

  • New DPMechGaussian class implementing the Gaussian mechanism, which achieves (epsilon,delta)-differential privacy by adding Gaussian noise to numeric responses calibrated by L2-norm sensitivity.
  • Refactoring of DPMechGaussian and DPMechLaplace underneath a new VIRTUAL class DPMechNumeric which contains common methods, dims slot (formerly dim changed because dim is a special slot for S4).

diffpriv 0.2.0

  • DPMechLaplace objects can now be initialized without specifying non-private target response dim. In such cases, the sensitivity sampler will perform an additional target probe to determine dim.


  • Sensitivity sampler methods no longer require oracles that return lists. Acceptable oracles may now return lists, matrices, data frames, numeric vectors, or char vectors. As a consequence some example code in docs, README and vignette, is simplified.


  • Initial release

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.4.2 by Benjamin Rubinstein, 4 years ago

https://github.com/brubinstein/diffpriv, http://brubinstein.github.io/diffpriv

Report a bug at https://github.com/brubinstein/diffpriv/issues

Browse source code at https://github.com/cran/diffpriv

Authors: Benjamin Rubinstein [aut, cre] , Francesco Aldà [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports gsl, methods, stats

Suggests randomNames, testthat, knitr, rmarkdown

See at CRAN