ContrastiveLeak

This is a PyTorch implementation of Quantifying and Mitigating Privacy Risks of Contrastive Learning, as described in our paper:

Xinlei He and Yang Zhang, Quantifying and Mitigating Privacy Risks of Contrastive Learning (CCS 2021)

Train contrastive classifiers:

0. pretrain resnet18 with STL10 unlabeled dataset

python3 run.py --gpu 0 --dataset STL10 --model resnet18 --adv_training no --mode target --pretrain yes

1. Finetune in downstream dataset, e.g., CIFAR10

   Pretraining stage:

   python3 run.py --gpu 0 --dataset CIFAR10 --model resnet18 --adv_training no --mode target
   python3 run.py --gpu 0 --dataset CIFAR10 --model resnet18 --adv_training no --mode shadow
   

   Linear stage:

   python3 classifier.py --batch_size 512 --dataset CIFAR10 --model resnet18 --mode target
   python3 classifier.py --batch_size 512 --dataset CIFAR10 --model resnet18 --mode shadow
   

Train normal classifiers with cross-entropy loss:

python3 ce_classifier.py --batch_size 512 --dataset CIFAR10 --model resnet18 --mode target
python3 ce_classifier.py --batch_size 512 --dataset CIFAR10 --model resnet18 --mode shadow

Conduct membership inference attack

method = ["CE", "SimCLR"] mia_type = ['nn-based', "metric-based", "label-only"]

python3 MIA.py --batch_size 512 --dataset CIFAR10 --model resnet18 --method SimCLR --mia_type nn-based

Note that for dataset that used for attribute inference, you may need to specify the original_label and aux_label

python3 MIA.py --batch_size 512 --dataset UTKFace --original_label Gender --aux_label Race --model resnet18 --method SimCLR --mia_type metric-based

Conduct attribute inference attack

python3 OL.py --batch_size 512 --dataset UTKFace --model resnet18 --original_label Gender --aux_label Race --method SimCLR 

Adversarial Training (Talos)

# Pretraining stage
python3 run.py --batch_size 512 --dataset UTKFace --model resnet18 --mode target --adv_training yes --adv_image augmented --adv_location embedding --adv_factor 1 --original_label Gender --aux_label Race 
python3 run.py --batch_size 512 --dataset UTKFace --model resnet18 --mode shadow --adv_training yes --adv_image augmented --adv_location embedding --adv_factor 1 --original_label Gender --aux_label Race 

# Linear stage
python3 classifier_with_adv_pretrained_SimCLR.py --batch_size 512 --dataset UTKFace --model resnet18 --mode target --adv_training yes --adv_image augmented --adv_location embedding --adv_factor 1 --original_label Gender --aux_label Race
python3 classifier_with_adv_pretrained_SimCLR.py --batch_size 512 --dataset UTKFace --model resnet18 --mode shadow --adv_training yes --adv_image augmented --adv_location embedding --adv_factor 1 --original_label Gender --aux_label Race

Membership Inference Attack Against Talos

python3 MIA_adv_simclr.py --batch_size 512 --dataset UTKFace --model resnet18  --original_label Gender --aux_label Race --adv_factor 1 --method SimCLR

Attribute Inference Attack Against Talos

python3 OL_adv_SimCLR.py --batch_size 512 --dataset UTKFace --model resnet18  --original_label Gender --aux_label Race --adv_factor 1 --method SimCLR

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{HZ21,
author = {Xinlei He and Yang Zhang},
title = {{Quantifying and Mitigating Privacy Risks of Contrastive Learning}},
booktitle = {{ACM SIGSAC Conference on Computer and Communications Security (CCS)}},
pages = {845-863},
publisher = {ACM},
year = {2021}
}