PLD-subsampling 0.1.1

Subsampling transformations and utilities for Privacy Loss Distribution (PLD) PMFs

PLD_subsampling

Implements and evaluates privacy amplification by subsampling for Privacy Loss Distribution (PLD) probability mass functions (PMFs). Generates CDF plots and epsilon ratio plots comparing analytical ground truth, dp-accounting, and our direct subsampling implementation.

Package layout

• PLD_subsampling/
  • PLD_subsampling_impl.py: Core subsampling primitives
    • stable_subsampling_loss: numerically stable loss mapping
    • exclusive_ccdf_from_pdf: CCDF helper (exclusive tail)
    • subsample_losses: transforms a PMF on a uniform loss grid
  • wrappers/dp_accounting_wrappers.py: Thin wrappers around dp-accounting (construct PLDs, amplify PLDs separately for remove/add), plus PMF bridge utilities
    • amplify_pld_separate_directions(base_pld, sampling_prob, return_pld=False): returns a dict with 'pmf_remove' and 'pmf_add'. If return_pld=True, attempts to build a PrivacyLossDistribution from the two PMFs, falling back to the dict if unsupported by the installed dp-accounting version.
  • testing/
    • analytic_Gaussian.py: Analytical PLD and epsilon(δ) formulas for Gaussian mechanism
    • test_utils.py: Experiment runners (run_experiment, run_multiple_experiments)
    • plot_utils.py: Plotting (CDF with focused x-range, epsilon ratio)
  • main.py: Runs experiments and saves figures to plots/

Quickstart

1. Create a virtual environment and install dependencies

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt

2. Editable install for local development (optional)

pip install -e .

3. Run experiments and generate plots

python -m PLD_subsampling.main

Figures are written to plots/ (treat this directory as build output).

Notes

• CDF plots automatically focus the main x-axis on the transition region and add slight y-padding to show the 0 and 1 limits clearly.
• Epsilon-ratio plots show method/GT vs analytical epsilon over log-scale epsilon.
• All heavy computations use vectorized NumPy operations with careful numerical handling in tail regions.

Build a package

python -m pip install --upgrade build
python -m build

Artifacts will be created under dist/. To upload to PyPI/TestPyPI, use twine with an API token.