LOngitudinal LOcal HAshing (LOLOHA)

Repository for the paper: Héber H. Arcolezi, Carlos Pinzón, Catuscia Palamidessi, Sébastien Gambs. "Frequency Estimation of Evolving Data Under Local Differential Privacy". In: Proceedings of the 26th International Conference on Extending Database Technology, EDBT 2023, Ioannina, Greece, March 28 - March 31, 2023. pp. 512–525. http://dx.doi.org/10.48786/edbt.2023.44.

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

@inproceedings{Arcolezi2023,
  author    = {Arcolezi,  Héber H. and Pinzón,  Carlos A and Palamidessi,  Catuscia and Gambs,  Sébastien},
  title     = {Frequency Estimation of Evolving Data Under Local Differential Privacy},
  booktitle = {Proceedings of the 26th International Conference on Extending Database
               Technology, {EDBT} 2023, Ioannina, Greece, March 28 - March 31, 2023},
  pages     = {512--525},
  publisher = {OpenProceedings.org},
  year      = {2023},
  doi       = {10.48786/EDBT.2023.44},
}

Codes & Datasets

All experiments in the paper are repeated over 20 iterations. Here we provide four Jupyter notebooks that use a reduced fraction of the respective dataset to decrease execution time through 5 iterations. Please use the whole dataset (frac=1) and all iterations (nb_seed=20) to fully reproduce the paper's results.

• The LDP folder has all developed longitudinal LDP protocols.
• The datasets folder has all used datastes.
• Experiments:
  • The Experiments_Adult.ipynb Jupyter notebook has the experimental evaluation with the Adult dataset.
  • The Experiments_Syn.ipynb Jupyter notebook has the experimental evaluation with the Synthetic dataset.
  • The Experiments_DB_MT.ipynb Jupyter notebook has the experimental evaluation with the DB_MT dataset.
  • The Experiments_DB_DE.ipynb Jupyter notebook has the experimental evaluation with the DB_DE dataset.
• Appendix:
  • The Appendix_Theoretical_Analysis.ipynb Jupyter notebook has the theoretical analysis of our LOLOHA protocol (privacy levels, estimator, variance, and the optimization of parameter g) and of state-of-the-art LDP protocols.
  • The Appendix_Variances.ipynb Jupyter notebook has the theoretical variances and the numerical analysis of variances (Fig. 2).

We have implemented LOLOHA mechanisms into our multi-freq-ldpy Python package.

Environment

Our codes were developed using Python 3 with numpy, pandas, and numba libraries. The versions are listed below:

• Python 3.8.8
• Numpy 1.23.1
• Pandas 1.2.4
• Numba 0.53.1

Contact

For any questions, please contact:

• Héber H. Arcolezi: heber.hwang-arcolezi [at] inria.fr
• Carlos Pinzón: carlos.pinzon [at] inria.fr

License

MIT