Prom-PATE

The official code of ICCV2023 paper "Exploring the Benefits of Visual Prompting in Differential Privacy"

Usage

Prepare your environment

This project is running on Python 3.8, but a higher version of Python should also work. Download required packages

pip install -r requirements.txt

Train Teacher model

For Cifar10 Dataset

cd pate
python pate_cifar10.py

For Cifar100 Dataset

cd pate
python pate_cifar100.py

For Blood_MNIST Dataset

cd pate
python pate_blood.py

Get noisy votes

For Cifar10 Dataset

epsilon = 1

cd noisycount
python noisy_and_count.py --dataset CIFAR10 \
    --preds_file cifar10 \
    --class_num 10 \
    --seed 8872574 \
    --sigma1 200 \
    --sigma2 50 \
    --threshold 600 \
    --delta 1e-5 \
    --queries 1000 \
    --eps e1 \

For Cifar100 Dataset

epsilon = 8

cd noisycount
python noisy_and_count.py --dataset CIFAR100 \
    --preds_file cifar100\
    --class_num 100 \
    --seed 8872574 \
    --sigma1 50 \
    --sigma2 10 \
    --threshold 122 \
    --delta 1e-5 \
    --queries 2000 \
    --eps e8

epsilon = 4

cd noisycount
python noisy_and_count.py --dataset CIFAR100 \
    --preds_file cifar100\
    --class_num 100 \
    --seed 8872574 \
    --sigma1 100 \
    --sigma2 25 \
    --threshold 200 \
    --delta 1e-5 \
    --queries 2000 \
    --eps e4

Train Student model by SSL

For different datasets, first modify the init.py file in Prompt-PATE\SSL\semilearn\core.

For Cifar10 Dataset

cd SSL
python train.py --c cifar10.yaml