PLD-subsampling 0.1.0

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.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
  • testing/
    • analytic_Gaussian.py: Analytical PLD and epsilon(δ) formulas for Gaussian mechanism
    • test_utils.py: Builds method variants for experiments, parameterized run_all_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. 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.