Experiments with Differentially Private Algorithms

This is the accompanying source code to my Bachelor's thesis at TUHH "Computational Verification of Differentially Private Algorithms" (2018).

Installation

The package and its dependencies can be installed in a virtual environment like this:

virtualenv -p python3 env
source env/bin/activate
pip3 install -e .

Usage

The GUI programs can be called with either of

diffpriv-single
diffpriv-reportnoisymax
diffpriv-svt

algorithms.py implements basic differentially private algorithms in terms of random distributions.

naive.py contains the same algorithms in their original forms, which produce single random values. They can be used for Monte-Carlo trials.

accuracy.py provides different accuracy estimates for the Sparse Vector Technique in a library. If executed as a script, it plots example graphs.

plot_* produce some example plots to illustrate the principles behind the algorithms or statistics about experimental data.

gui_* contain the source code for the interactive experiments.

data_* retrieve and pre-process test data to be usable in the computation-intensive experiments.py. The unpacked data requires about 590 MB. Use in this order: data_get.sh, data_histogram*, data_flatten. data_count* is there to verify the item counts.

experiments.py must be used interactively with

python3 -i experiments.py

where one can choose which part to run. Preparing the data and computing probabilities may take multiple days on mid-range hardware. The intermediate files take up around 5.5 GB.