AhmedSalem2 / ML-LeaksLinks
Code for the paper "ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models"
☆85Updated 3 years ago
Alternatives and similar repositories for ML-Leaks
Users that are interested in ML-Leaks are comparing it to the libraries listed below
Sorting:
- ☆45Updated 5 years ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆194Updated 7 years ago
- ☆23Updated 2 years ago
- Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.☆60Updated 6 years ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models☆126Updated last year
- Code for ML Doctor☆88Updated 9 months ago
- Code for "Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment" (CCS 2019)☆46Updated 5 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆53Updated 6 years ago
- Code for the paper: Label-Only Membership Inference Attacks☆65Updated 3 years ago
- Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.☆62Updated 8 months ago
- paper code☆27Updated 4 years ago
- Implementation of the Model Inversion Attack introduced with Model Inversion Attacks that Exploit Confidence Information and Basic Counte…☆85Updated 2 years ago
- ICML 2022 code for "Neurotoxin: Durable Backdoors in Federated Learning" https://arxiv.org/abs/2206.10341☆73Updated 2 years ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆56Updated 2 years ago
- Attacking a dog vs fish classification that uses transfer learning inceptionV3☆70Updated 7 years ago
- [ICLR 2023, Best Paper Award at ECCV’22 AROW Workshop] FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning☆56Updated 5 months ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆135Updated 2 years ago
- Code & supplementary material of the paper Label Inference Attacks Against Federated Learning on Usenix Security 2022.☆83Updated last year
- Github Repo for AAAI 2023 paper: On the Vulnerability of Backdoor Defenses for Federated Learning☆37Updated 2 years ago
- ☆53Updated last year
- A sybil-resilient distributed learning protocol.☆103Updated last year
- ☆31Updated 9 months ago
- Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (RAID 2018)☆47Updated 6 years ago
- This is a simple backdoor model for federated learning.We use MNIST as the original data set for data attack and we use CIFAR-10 data set…☆14Updated 4 years ago
- ☆29Updated last year
- Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)☆47Updated 3 years ago
- Privacy attacks on Split Learning☆42Updated 3 years ago
- Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)☆50Updated 2 years ago
- ☆25Updated 3 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆32Updated 3 years ago