uds-lsv / SIDPLinks
Robust Differentially Private Training of Deep Neural Networks
☆12Updated 5 years ago
Alternatives and similar repositories for SIDP
Users that are interested in SIDP are comparing it to the libraries listed below
Sorting:
- simple Differential Privacy in PyTorch☆49Updated 5 years ago
- Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent (ICLR 2021)☆21Updated 4 years ago
- Algorithms to recover input data from their gradient signal through a neural network☆311Updated 2 years ago
- ☆80Updated 3 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆56Updated 6 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆49Updated 7 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆135Updated 3 years ago
- Differential Privacy Preservation in Deep Learning under Model Attacks☆135Updated 4 years ago
- DBA: Distributed Backdoor Attacks against Federated Learning (ICLR 2020)☆202Updated 4 years ago
- [NeurIPS 2019] Deep Leakage From Gradients☆474Updated 3 years ago
- autodp: A flexible and easy-to-use package for differential privacy☆278Updated 2 years ago
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆152Updated 3 years ago
- Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness (IJCAI'19).☆13Updated 4 years ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆200Updated 8 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 4 years ago
- ☆45Updated 6 years ago
- Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy☆18Updated last year
- The code for "Improved Deep Leakage from Gradients" (iDLG).☆165Updated 4 years ago
- ☆16Updated 6 years ago
- Code for Auditing DPSGD☆37Updated 3 years ago
- ☆67Updated 6 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆51Updated 7 years ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆21Updated 5 years ago
- A library for running membership inference attacks against ML models☆152Updated 3 years ago
- Code for NDSS 2021 Paper "Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses Against Federated Learning"☆148Updated 3 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆32Updated 3 years ago
- ☆16Updated 6 years ago
- ☆24Updated 3 years ago
- A list of papers using/about Federated Learning especially malicious client and attacks.☆12Updated 5 years ago
- PyTorch for benchmarking communication-efficient distributed SGD optimization algorithms☆78Updated 4 years ago