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@example.com.
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:
The output is written to
_tmp/regtest/results.html, and can be opened with a browser.
R analysis (
Demo dependencies (
These are necessary if you want to test changes to the code.
Python client (
To run tests:
This currently runs Python unit tests, lints Python source files, and runs R unit tests.
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.
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.
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