lisaoakley / wmc_for_privacy_and_accuracy Goto Github PK

This repo contains the tools ExactAcc and ExactDP for running tight privacy and accuracy bound synthesis as described in the paper:

Oakley, Lisa, Steven Holtzen, and Alina Oprea. "Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting," 2024 IEEE 37th Computer Security Foundations Symposium (CSF), 2024.

This repo also contains all the experiment and plotting scripts to reproduce the results from the paper. To cite, please use:

@article{oakley2024synthesizing,
  title={Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting},
  author={Oakley, Lisa and Holtzen, Steven and Oprea, Alina},
  booktitle={2024 IEEE 37th Computer Security Foundations Symposium (CSF)}, 
  year={2024}
  }

./plotting_scripts contains scripts for plotting figures in the paper. In the README.md, and at the top of each script, there is a comment which tells you which experiment scripts need to be run before the plot can be made. The plotting scripts must be run from the project home directory (e.g. python plotting_scripts/Fig2.py).

./experiment_scripts contains all the scripts for running the experiments whose data appears in the paper. The experiment scripts must be run from the project home directory (e.g. ./experiment_scripts/RR_ExactDP_Restricted.sh).

ExactDP and ExactAcc can be run directly with different parameters specified in the command line. Run with -h to see which options are available.

To run the Weighted Model Counting (WMC) solution, we use the Dice probabilistic programming language.

To run the DTMC model checker experiments, we use the Stormpy python package for the Storm model checker.