forked from 626_privacy/tensorflow_privacy
61 lines
2.1 KiB
Markdown
61 lines
2.1 KiB
Markdown
Scripts in support of the paper "Scalable Private Learning with PATE" by Nicolas
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Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar
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Erlingsson (ICLR 2018, https://arxiv.org/abs/1802.08908).
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### Requirements
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* Python, version ≥ 2.7
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* absl (see [here](https://github.com/abseil/abseil-py), or just type `pip install absl-py`)
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* matplotlib
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* numpy
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* scipy
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* sympy (for smooth sensitivity analysis)
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* write access to the current directory (otherwise, output directories in download.py and *.sh
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scripts must be changed)
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## Reproducing Figures 1 and 5, and Table 2
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Before running any of the analysis scripts, create the data/ directory and download votes files by running\
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`$ python download.py`
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To generate Figures 1 and 5 run\
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`$ sh generate_figures.sh`\
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The output is written to the figures/ directory.
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For Table 2 run (may take several hours)\
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`$ sh generate_table.sh`\
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The output is written to the console.
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For data-independent bounds (for comparison with Table 2), run\
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`$ sh generate_table_data_independent.sh`\
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The output is written to the console.
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## Files in this directory
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* generate_figures.sh — Master script for generating Figures 1 and 5.
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* generate_table.sh — Master script for generating Table 2.
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* generate_table_data_independent.sh — Master script for computing data-independent
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bounds.
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* rdp_bucketized.py — Script for producing Figure 1 (right) and Figure 5 (right).
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* rdp_cumulative.py — Script for producing Figure 1 (middle) and Figure 5 (left).
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* smooth_sensitivity_table.py — Script for generating Table 2.
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* utility_queries_answered — Script for producing Figure 1 (left).
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* plot_partition.py — Script for producing partition.pdf, a detailed breakdown of privacy
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costs for Confident-GNMax with smooth sensitivity analysis (takes ~50 hours).
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* plots_for_slides.py — Script for producing several plots for the slide deck.
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* download.py — Utility script for populating the data/ directory.
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* plot_ls_q.py is not used.
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All Python files take flags. Run script_name.py --help for help on flags.
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