This repository accompanies the paper DatashareNetwork: A Decentralized Privacy-Preserving Search Engine for Investigative Journalists by Kasra EdalatNejad (SPRING Lab, EPFL), Wouter Lueks (SPRING Lab, EPFL), Julien Pierre Martin, Soline Ledésert (ICIJ), Anne L'Hôte (ICIJ), Bruno Thomas (ICIJ), Laurent Girod (SPRING Lab, EPFL) and Carmela Troncoso (SPRING Lab, EPFL), which will be presented at USENIX 2020.
This repository contains a proof of concept implementation of cryptographic primitives of DatashareNetwork and aims to enable reproducibility of measurements in the paper.
You can find the original paper at USENIX conference and the extended edition at arXiv.
Install petlib's requirements
sudo apt-get install python3-dev libssl-dev libffi-dev
(Optional) Create a virtual environment
python3 -m venv venv
source venv/bin/activate
Install the requirements.
pip3 install -r requirements.txt
To run the tests, run the following command.
python3 -m pytest -s
Anonymous credentials have been moved to the SSCred library.
Two benchmarks are provided
benchmark-mspsi.py: Benchmark for interacting with one journalist. This benchmark aims to measure the time to make and reply to a query.benchmark-mspsi-many-journalist.py: Benchmark for searching the corpora of many journalists.
These benchmarks run the protocol and record the timing in the benchmark-mspsi-{time-stamp}.json file.
A jupyter notebook (notebooks\benchmark_plotting_mspsi.ipynb) is provided to visualize the single journalist data.
To use the notebook: jupyter lab notebooks\benchmark_plotting_mspsi.ipynb
Then open the notebook with Jupyter's interface. Run all cells to generate the performance plots of mspsi.