Code for ML Doctor
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Updated
Nov 30, 2023 - Python
Code for ML Doctor
Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)
[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" by Chongyu Fan*, Jiancheng Liu*, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu
🔒 Implementation of Shokri et al(2016) "Membership Inference Attacks against Machine Learning Models"
Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
[NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu
reveal the vulnerabilities of SplitNN
Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)
Membership inference against Federated learning.
Differential Privacy Protection against MembershipInference Attack on Machine Learning for Genomic Data
DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.
Collection of tools and resources for managing the statistical disclosure control of trained machine learning models
Accompanying code for "Disparate Vulnerability to Membership Inference Attacks"
Codebase for Active Membership Inference Attack under Local Differential Privacy in Federated Learning
Min-K%++: Improved baseline for detecting pre-training data of LLMs https://arxiv.org/abs/2404.02936
Performing membership inference attack (MIA) against Korean language models (LMs).
Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
Defending Privacy Against More Knowledgeable Membership Inference Attackers
FederBoost's Federated Gradient Boosting Decision Tree Algorithm, Federated enabled Membership Inference
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