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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
Last synced: about 1 month ago
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Easy differential privacy in R
- Host: GitHub
- URL: https://github.com/brubinstein/diffpriv
- Owner: brubinstein
- License: other
- Created: 2017-06-28T07:07:04.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-07-01T05:30:18.000Z (over 2 years ago)
- Last Synced: 2024-03-15T05:45:55.718Z (9 months ago)
- Topics: data-science, differential-privacy, diffpriv, machine-learning, r, r-package, statistics
- Language: R
- Homepage: https://brubinstein.github.io/diffpriv/
- Size: 9.95 MB
- Stars: 65
- Watchers: 3
- Forks: 14
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
- AwesomeResponsibleAI - diffpriv
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"
```
[](commits/master)
[](https://cran.r-project.org/package=diffpriv)
[](https://travis-ci.org/brubinstein/diffpriv)
[](https://codecov.io/github/brubinstein/diffpriv?branch=master)
[](http://choosealicense.com/licenses/mit/)
[](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.