haiphanNJIT / SecureSGDLinks
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness (IJCAI'19).
☆13Updated 4 years ago
Alternatives and similar repositories for SecureSGD
Users that are interested in SecureSGD are comparing it to the libraries listed below
Sorting:
- Implementation of calibration bounds for differential privacy in the shuffle model☆22Updated 4 years ago
- Code for the paper "Bayesian Differential Privacy for Machine Learning"☆22Updated 4 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆33Updated 4 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆49Updated 7 years ago
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…☆41Updated last year
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 4 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆48Updated 6 years ago
- Differential Privacy Preservation in Deep Learning under Model Attacks☆135Updated 4 years ago
- Repository for Federated Learning with Differential Privacy☆11Updated 3 years ago
- Curated notebooks on how to train neural networks using differential privacy and federated learning.☆68Updated 4 years ago
- Local Differential Privacy for Federated Learning☆16Updated 2 years ago
- An implementation of Deep Learning with Differential Privacy☆25Updated 2 years ago
- Implementation of Shuffled Model of Differential Privacy in Federated Learning." AISTATS, 2021.☆18Updated 2 years ago
- Improved DP-SGD for optimizing☆18Updated 6 years ago
- This is an implementation for paper "A Hybrid Approach to Privacy Preserving Federated Learning" (https://arxiv.org/pdf/1812.03224.pdf)☆21Updated 4 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆53Updated 6 years ago
- A list of papers using/about Federated Learning especially malicious client and attacks.☆12Updated 4 years ago
- ☆16Updated 6 years ago
- Federated Learning and Membership Inference Attacks experiments on CIFAR10☆22Updated 5 years ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆56Updated 2 years ago
- Secure and utility-aware data collection with condensed local differential privacy☆17Updated 5 years ago
- ☆37Updated 3 years ago
- A sybil-resilient distributed learning protocol.☆103Updated last year
- 基于《A Little Is Enough: Circumventing Defenses For Distributed Learning》的联邦学习攻击模型☆63Updated 5 years ago
- Applying Laplace and exponential mechanisms to add random noise to data for differential privacy. Plotting MSE vs. epsilon.☆29Updated 4 years ago
- ☆25Updated 3 years ago
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆150Updated 2 years ago
- ☆35Updated 2 years ago
- code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"☆31Updated 2 years ago
- Differentially Private Federated Learning: A Client Level Perspective☆12Updated 5 years ago