inspire-group / membership-inference-evaluationLinks
Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models
☆126Updated last year
Alternatives and similar repositories for membership-inference-evaluation
Users that are interested in membership-inference-evaluation are comparing it to the libraries listed below
Sorting:
- A library for running membership inference attacks against ML models☆148Updated 2 years ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆194Updated 7 years ago
- Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.☆60Updated 6 years ago
- ☆45Updated 5 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆135Updated 2 years ago
- ☆23Updated 2 years ago
- Code for ML Doctor☆91Updated 10 months ago
- Code for the paper: Label-Only Membership Inference Attacks☆65Updated 3 years ago
- Implementation of the Model Inversion Attack introduced with Model Inversion Attacks that Exploit Confidence Information and Basic Counte…☆84Updated 2 years ago
- Code for the paper "ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models"☆85Updated 3 years ago
- Code for Auditing DPSGD☆37Updated 3 years ago
- This repo implements several algorithms for learning with differential privacy.☆108Updated 2 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆32Updated 3 years ago
- Python package to create adversarial agents for membership inference attacks againts machine learning models☆47Updated 6 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆37Updated 6 years ago
- Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.☆62Updated 8 months ago
- ☆31Updated 9 months ago
- Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)☆50Updated 2 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆53Updated 6 years ago
- Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)☆48Updated 3 years ago
- 🔒 Implementation of Shokri et al(2016) "Membership Inference Attacks against Machine Learning Models"☆36Updated 2 years ago
- simple Differential Privacy in PyTorch☆48Updated 5 years ago
- Official implementation of "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" (CCS 2020)☆48Updated 3 years ago
- Example of the attack described in the paper "Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization"☆21Updated 5 years ago
- ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation☆52Updated 3 years ago
- Implementation of membership inference and model inversion attacks, extracting training data information from an ML model. Benchmarking …☆103Updated 5 years ago
- Privacy attacks on Split Learning☆42Updated 3 years ago
- ☆80Updated 3 years ago
- Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (RAID 2018)☆47Updated 6 years ago
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆151Updated 2 years ago