Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
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Updated
Nov 15, 2017 - Python
Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
Differential Privacy Protection against MembershipInference Attack on Machine Learning for Genomic Data
Testing membership inference attacks on Deep learning models (LSTM, CNN);
Membership inference against Federated learning.
The source code for ICML2021 paper When Does Data Augmentation Help With Membership Inference Attacks?
Accompanying code for "Disparate Vulnerability to Membership Inference Attacks"
Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)
Defending Privacy Against More Knowledgeable Membership Inference Attackers
reveal the vulnerabilities of SplitNN
An implementation of ICLR 22 paper "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" in PyTorch
An implementation of loss thresholding attack to infer membership status as described in paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting" (CSF 18) in PyTorch.
🔒 Implementation of Shokri et al(2016) "Membership Inference Attacks against Machine Learning Models"
A mitigation method against privacy violation attacks on face recognition systems
Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)
Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.
Code for ML Doctor
FederBoost's Federated Gradient Boosting Decision Tree Algorithm, Federated enabled Membership Inference
Codebase for Active Membership Inference Attack under Local Differential Privacy in Federated Learning
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