A mitigation method against privacy violation attacks on face recognition systems
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Updated
Jan 10, 2023 - Python
A mitigation method against privacy violation attacks on face recognition systems
An implementation of ICLR 22 paper "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" in PyTorch
Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
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Evaluating the impact of entropy, maximum posterior probability, and standard deviation of probability vector in mitigating black-box membership inference attack
The source code of the paper "Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks"(EuroS&P 2024)
Microsoft's Membership Inference Competition (MICO) for CIFAR10 using shadow models.
This repository accompanies the paper "SynthShield: Leveraging Synthetic Distributions to Enhance Privacy Against Membership Inference" currently under review at the International Conference on Pattern Recognition (ICPR). It contains the main code used in applying and analysing the SynthShield technique analysed in the paper.
DP-UTIL: A Comprehensive Utility Analysis of Differential Privacy in Machine Learning
Privacy in Practice: Private COVID-19 Detection in X-Ray Images
Defending Privacy Against More Knowledgeable Membership Inference Attackers
Source code for our IJCAI-ECAI 2022 paper "To Trust or Not To Trust Prediction Scores for Membership Inference Attacks"
Testing membership inference attacks on Deep learning models (LSTM, CNN);
Performing membership inference attack (MIA) against Korean language models (LMs).
DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.
Universität des Saarlandes - Privacy Enhancing Technologies 2021 - Semester Project
Implementations on Security and Privacy in ML; Evasion Attack, Model Stealing, Model Poisoning, Membership Inference Attacks, ...
Membership inference against Federated learning.
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.
Accompanying code for "Disparate Vulnerability to Membership Inference Attacks"
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