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
Testing membership inference attacks on Deep learning models (LSTM, CNN);
The source code of the paper "Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks"(EuroS&P 2024)
Bachelor's Thesis on Membership Inference Attacks
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.
This repository contains personal notes and summaries on Secure and Private AI
Evaluating the impact of entropy, maximum posterior probability, and standard deviation of probability vector in mitigating black-box membership inference attack
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.
The official implementation of the paper "Data Contamination Calibration for Black-box LLMs" (ACL 2024)
DP-UTIL: A Comprehensive Utility Analysis of Differential Privacy in Machine Learning
Performing membership inference attack (MIA) against Korean language models (LMs).
Universität des Saarlandes - Privacy Enhancing Technologies 2021 - Semester Project
Defending Privacy Against More Knowledgeable Membership Inference Attackers
Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
Implementations on Security and Privacy in ML; Evasion Attack, Model Stealing, Model Poisoning, Membership Inference Attacks, ...
Privacy in Practice: Private COVID-19 Detection in X-Ray Images
Min-K%++: Improved baseline for detecting pre-training data of LLMs https://arxiv.org/abs/2404.02936
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