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DatashareNetwork proof of concept

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.

Installation

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

Anonymous credentials have been moved to the SSCred library.

Evaluation

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.

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Proof of concept code for DatashareNetwork paper.

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