vrt1shjwlkr / AAAI21-MIA-DefenseLinks
Code for AAAI 2021 Paper "Membership Privacy for Machine Learning Models Through Knowledge Transfer"
☆11Updated 4 years ago
Alternatives and similar repositories for AAAI21-MIA-Defense
Users that are interested in AAAI21-MIA-Defense are comparing it to the libraries listed below
Sorting:
- Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.☆60Updated 6 years ago
- Membership Inference Attacks and Defenses in Neural Network Pruning☆28Updated 3 years ago
- ☆46Updated 6 years ago
- Code for the paper: Label-Only Membership Inference Attacks☆66Updated 4 years ago
- ☆32Updated last year
- ☆54Updated 4 years ago
- Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)☆48Updated 3 years ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆57Updated 2 years ago
- This repo implements several algorithms for learning with differential privacy.☆111Updated 3 years ago
- [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…☆36Updated 3 years ago
- The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".☆35Updated 3 years ago
- A pytorch implementation of the paper "Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage".☆62Updated 3 years ago
- Adversarial attacks and defenses against federated learning.☆20Updated 2 years ago
- Code for the paper "ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models"☆85Updated 4 years ago
- Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.☆67Updated last year
- ☆70Updated 3 years ago
- ☆51Updated 4 years ago
- ☆19Updated 2 years ago
- ☆26Updated 6 years ago
- ☆55Updated 2 years ago
- [AAAI'23] Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning☆27Updated 2 years ago
- ☆30Updated 5 years ago
- [USENIX Security 2022] Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture☆17Updated 3 years ago
- ☆36Updated 3 years ago
- ☆15Updated last year
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆152Updated 3 years ago
- CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)☆73Updated 4 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆56Updated 6 years ago
- ☆10Updated 3 years ago
- Differentially Private Federated Learning: A Client Level Perspective☆12Updated 6 years ago