tensorflow_privacy/research/pate_2018/ICLR2018

Scripts in support of the paper "Scalable Private Learning with PATE" by Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar Erlingsson (ICLR 2018, https://arxiv.org/abs/1802.08908).

Requirements

• Python, version ≥ 2.7
• absl (see here, or just type pip install absl-py)
• matplotlib
• numpy
• scipy
• sympy (for smooth sensitivity analysis)
• write access to the current directory (otherwise, output directories in download.py and *.sh scripts must be changed)

Reproducing Figures 1 and 5, and Table 2

Before running any of the analysis scripts, create the data/ directory and download votes files by running
$ python download.py

To generate Figures 1 and 5 run
$ sh generate_figures.sh
The output is written to the figures/ directory.

For Table 2 run (may take several hours)
$ sh generate_table.sh
The output is written to the console.

For data-independent bounds (for comparison with Table 2), run
$ sh generate_table_data_independent.sh
The output is written to the console.

Files in this directory

• generate_figures.sh — Master script for generating Figures 1 and 5.

• generate_table.sh — Master script for generating Table 2.

• generate_table_data_independent.sh — Master script for computing data-independent bounds.

• rdp_bucketized.py — Script for producing Figure 1 (right) and Figure 5 (right).

• rdp_cumulative.py — Script for producing Figure 1 (middle) and Figure 5 (left).

• smooth_sensitivity_table.py — Script for generating Table 2.

• utility_queries_answered — Script for producing Figure 1 (left).

• plot_partition.py — Script for producing partition.pdf, a detailed breakdown of privacy costs for Confident-GNMax with smooth sensitivity analysis (takes ~50 hours).

• plots_for_slides.py — Script for producing several plots for the slide deck.

• download.py — Utility script for populating the data/ directory.

• plot_ls_q.py is not used.

All Python files take flags. Run script_name.py --help for help on flags.