diffpriv

Easy differential privacy in R

https://github.com/brubinstein/diffpriv

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Easy differential privacy in R

README.Rmd

---
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. 

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