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https://github.com/brubinstein/diffpriv

Easy differential privacy in R
https://github.com/brubinstein/diffpriv

data-science differential-privacy diffpriv machine-learning r r-package statistics

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Easy differential privacy in R

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README 

        
---

output:

  github_document:

    html_preview: false

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

set.seed(3033362) # for reproducibility

```



# diffpriv 



```{r, echo = FALSE}                       

#version <- as.vector(read.dcf('DESCRIPTION')[, 'Version'])     

#version <- gsub('-', '.', version)                       

version <- "0.4.2.9000"

```                       



```{r, echo = FALSE}         

#dep <- as.vector(read.dcf('DESCRIPTION')[, 'Depends'])         

#m <- regexpr('R *\\(>= \\d+.\\d+.\\d+\\)', dep)         

#rm <- regmatches(dep, m)         

#rvers <- gsub('.*(\\d+.\\d+.\\d+).*', '\\1', rm)         

rvers <- "3.4.0"

```



[![packageversion](https://img.shields.io/badge/Package%20version-`r version`-orange.svg?style=flat-square)](commits/master)

[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/diffpriv)](https://cran.r-project.org/package=diffpriv)

[![Travis Build Status](https://travis-ci.org/brubinstein/diffpriv.svg?branch=master)](https://travis-ci.org/brubinstein/diffpriv)

[![Coverage Status](https://img.shields.io/codecov/c/github/brubinstein/diffpriv/master.svg)](https://codecov.io/github/brubinstein/diffpriv?branch=master)

[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](http://choosealicense.com/licenses/mit/)

[![minimal R version](https://img.shields.io/badge/R%3E%3D-`r rvers`-6666ff.svg)](https://cran.r-project.org/)



## Overview



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.



## Installation



Obtaining `diffpriv` is easy. From within R:



```{r eval=FALSE}

## Install the release version of diffpriv from CRAN:

install.packages("diffpriv")



## Install the latest development version of diffpriv from GitHub:

install.packages("devtools")

devtools::install_github("brubinstein/diffpriv")

```



## Example



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:



```{r example-1}

## 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`.



```{r example-2}

## target seeks to release a numeric, so we'll use the Laplace mechanism---a

## standard generic mechanism for privatizing numeric responses

library(diffpriv)

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](http://brubinstein.github.io/diffpriv/articles/diffpriv.pdf)) 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.



```{r example-3}

## 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)

mech@sensitivity    ## DPMech and subclasses are S4: slots accessed via @

```



With a sensitivity-calibrated mechanism in hand, we can release private 

responses on a dataset `X`, displayed alongside the non-private response 

for comparison:



```{r example-4}

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))

```



## 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](http://brubinstein.github.io/diffpriv/articles/bernstein.pdf) 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 

  `DPMech@target` to estimate sensitivity to data perturbation. Running 

  mechanisms with obtained sensitivities yield random differential privacy.



Read the [package vignette](http://brubinstein.github.io/diffpriv/articles/diffpriv.pdf) for more, or [news](http://brubinstein.github.io/diffpriv/news/index.html)

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:



> Benjamin I. P. Rubinstein and Francesco Aldà. "Pain-Free Random Differential 

> Privacy with Sensitivity Sampling", to appear in the 34th International

> Conference on Machine Learning (ICML'2017), 2017.



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.