Experiments of the paper "Revisiting the attacker's knowledge in inference attacks against Searchable Symmetric Encryption"

Link to the paper: https://eprint.iacr.org/2023/1883

Authors: Marc Damie, Jean-Benoist Leger, Florian Hahn, and Andreas Peter.

For any question about the code or the paper, contact Marc Damie.

Install

The install process is straightforward: bash setup.sh. This script installs the Python dependencies and download the datasets.

Reproduce

To repoduce our results, you need to run python3 generate_results.py. This script launches all the experiments one by one. The results will be stored in multiple CSV files in a results folder.

To generate the figures, you need to run python3 generate_figures.py. This script generates all figures one by one using the CSV files generated by the previous script.

Repository structure

The folder src/ contains the following elements:

• document_extraction.py contains the functions to process the datasets. Each dataset has a different format so there are dedicated functions for each datasets.
• keyword_extraction.py contains the functions to process the extracted documents. These functions use multiprocessing to extract keywords efficiently.
• simulation_utils.py contains all auxiliary functions necessary to simulate attacks; e.g., adversary knowledge generation.
• attacks/ contains the functions to perform the score and IHOP attacks.