On the Risks of Collecting Multidimensional Data Under LDP

Héber H. Arcolezi, Sébastien Gambs, Jean-François Couchot, Catuscia Palamidessi. "On the Risks of Collecting Multidimensional Data Under Local Differential Privacy". PVLDB, 16(5): 1126 - 1139, 2023. doi: 10.14778/3579075.3579086.

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

@article{Arcolezi2023,
  doi = {10.14778/3579075.3579086},
  url = {https://doi.org/10.14778/3579075.3579086},
  year = {2023},
  month = jan,
  publisher = {Association for Computing Machinery ({ACM})},
  volume = {16},
  number = {5},
  pages = {1126--1139},
  author = {H{\'{e}}ber H. Arcolezi and S{\'{e}}bastien Gambs and Jean-Fran{\c{c}}ois Couchot and Catuscia Palamidessi},
  title = {On the Risks of Collecting Multidimensional Data Under Local Differential Privacy},
  journal = {Proceedings of the {VLDB} Endowment}
}

Codes

• The attack_SMP folder has the codes for reproducing the attacks to the SMP solution.
• The attack_RSpFD folder has the codes for reproducing the attacks to the RS+FD solution.
• The countermeasure_RSpRFD folder has the codes for reproducing the experiments/attacks of our countermeasure RS+RFD solution.

Datasets

The datasets folder has the following (pre-processed) datasets.

• ACSEmployement
• Adult

To Do

• I am slowly cleaning/generalizing the codes + documentation.
• Implement RS+RFD in the multi-freq-ldpy package.

Environment

I mainly used Python 3 with numpy, pandas, numba, multi-freq-ldpy, and ray libaries. The versions I use are listed below:

• Python 3.8.8
• Numpy 1.23.1
• Pandas 1.2.4
• Numba 0.53.1
• Multi-freq-ldpy 0.2.4
• Ray 1.11.0

Contact

For any question, please contact Héber H. Arcolezi: heber.hwang-arcolezi [at] inria.fr

License

MIT