RAPPOR
RAPPOR is a novel privacy technology that allows inferring statistics about populations while preserving the privacy of individual users.
This repository contains simulation and analysis code in Python and R.
For a detailed description of the algorithms, see the paper and links below.
Feel free to send feedback to [email protected].
Running the Demo
Although the Python and R libraries should be portable to any platform, our end-to-end demo has only been tested on Linux.
If you don't have a Linux box handy, you can view the generated output.
To setup your enviroment there are some packages and R dependencies. There is a setup script to install them:
$ ./setup.sh
Then to build the native components run:
$ ./build.sh
This compiles and tests the fastrand C extension module for Python, which
speeds up the simulation.
Finally to run the demo run: $ ./demo.sh
The demo strings together the Python and R code. It:
- Generates simulated input data with different distributions
- Runs it through the RAPPOR privacy-preserving reporting mechanisms
- Analyzes and plots the aggregated reports against the true input
The output is written to _tmp/regtest/results.html, and can be opened with a
browser.
Dependencies
R analysis (analysis/R):
Demo dependencies (demo.sh):
These are necessary if you want to test changes to the code.
Python client (client/python):
- None. You should be able to just import the
rappor.pyfile.
Platform:
- R: tested on R 3.0.
- Python: tested on Python 2.7.
- OS: the shell script tests have been tested on Linux, but may work on Mac/Cygwin. The R and Python code should work on any OS.
Development
To run tests:
$ ./test.sh
This currently runs Python unit tests, lints Python source files, and runs R unit tests.
API
rappor.py is a tiny standalone Python file, and you can easily copy it into a
Python program.
NOTE: Its interface is subject to change. We are in the demo stage now, but if there's demand, we will document and publish the interface.
The R interface is also subject to change.
The fastrand C module is optional. It's likely only useful for simulation of
thousands of clients. It doesn't use cryptographically strong randomness, and
thus should not be used in production.
Directory Structure
analysis/
R/ # R code for analysis
cpp/ # Fast reimplementations of certain analysis
# algorithms
apps/ # Web apps to help you use RAPPOR (using Shiny)
bin/ # Command line tools for analysis.
client/ # Client libraries
python/ # Python client library
rappor.py
...
cpp/ # C++ client library
encoder.cc
...
doc/ # Documentation
tests/ # Tools for regression tests
compare_dist.R # Test helper for single variable analysis
gen_true_values.R # Generate test input
make_summary.py # Generate an HTML report for the regtest
rappor_sim.py # RAPPOR client simulation
regtest_spec.py # Specification of test cases
...
build.sh # Build scripts (docs, C extension, etc.)
demo.sh # Quick demonstration
docs.sh # Generate docs form the markdown in doc/
gh-pages/ # Where generated docs go. (A subtree of the branch gh-pages)
pipeline/ # Analysis pipeline code.
regtest.sh # End-to-end regression tests, including client
# libraries and analysis
setup.sh # Install dependencies (for Linux)
test.sh # Test runner
Documentation
Publications
- RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
- Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries
Links
- Google Blog Post about RAPPOR
- RAPPOR implementation in Chrome
- This is a production quality C++ implementation, but it's somewhat tied to Chrome, and doesn't support all privacy parameters (e.g. only a few values of p and q). On the other hand, the code in this repo is not yet production quality, but supports experimentation with different parameters and data sets. Of course, anyone is free to implement RAPPOR independently as well.
- Mailing list: [email protected]