ripankundu / ldp-fairness-impact Goto Github PK

Repository for the paper: Arcolezi, H.H., Makhlouf, K., Palamidessi, C. (2023). (Local) Differential Privacy has NO Disparate Impact on Fairness. In: Atluri, V., Ferrara, A.L. (eds) Data and Applications Security and Privacy XXXVII. DBSec 2023. Lecture Notes in Computer Science, vol 13942. Springer, Cham. https://doi.org/10.1007/978-3-031-37586-6_1.

If our codes and work are useful to you, we would appreciate a reference to:

@incollection{Arcolezi2023,
  doi = {10.1007/978-3-031-37586-6_1},
  url = {https://doi.org/10.1007/978-3-031-37586-6_1},
  year = {2023},
  publisher = {Springer Nature Switzerland},
  pages = {3--21},
  author = {H{\'{e}}ber H. Arcolezi and Karima Makhlouf and Catuscia Palamidessi},
  title = {(Local) Differential Privacy has {NO} Disparate Impact on~Fairness},
  booktitle = {Data and Applications Security and Privacy {XXXVII}}
}

Our codes were developed using Python 3 with mainly numpy, pandas, lightgbm, and multi-freq-ldpy libraries. The versions we used are listed below:

• Python 3.9.13
• Numpy 1.21.5
• Pandas 1.4.4
• Lightgbm 3.3.5
• Multi-freq-ldpy 0.2.4

This repository is organized/ordered in several Jupyter Notebook files as:

• 0_Preprocess_Datasets.ipynb: Notebook for pre-processing (cleaning, encoding) three original datasets (i.e., Adult, ACSCoverage, LSAC). The pre-processed datasets are saved in the datasets folder.
• 1_BO_NonDP.ipynb: Notebook for conducting the Bayesian Optimization (BO) to find the local optimal LGBM hyperparameters using original data (i.e., no LDP). Results are saved in CSV format in the results folder.
• 2_Exp_XXX: Notebooks for carrying out all experiments (repeated over nb_seed=20 iterations) of the paper assuming the mechanism XXX (e.g., NonDP as the baseline, GRR, OUE, etc). Results are saved in CSV and numpy formats in the corresponding results folder. The notebook 2_Exp_All_LDP is generic and runs all LDP protocols.
• 3_Final_Results.ipynb: Notebook with codes to plot the final results illustrated in the main paper.
• 4_Appendix_Experiments.ipynb: Notebook for carrying out the additional set of experiments (repeated over nb_seed=20 iterations) with all LDP protocols. Results are also saved in CSV format in the results folder.
• 4_Appendix_Results.ipynb: Notebook with codes to plot the final results illustrated in the appendix of the full paper (in arXiv).

Some functions used in the Jupyter Notebooks are imported from functions.py.

We are slowly cleaning/generalizing the codes + documentation.

• We use the LDP protocols implemented in our multi-freq-ldpy library.
• We use the reconstructed Adult and ACSCoverage datasets from the folktables library.
• We use the LSAC dataset.

For any questions, please contact:

• Héber H. Arcolezi: heber.hwang-arcolezi [at] inria.fr
• Karima Makhlouf: karima.makhlouf [at] lix.polytechnique.fr

MIT