mijungi / dpem_code
☆8Updated 7 years ago
Alternatives and similar repositories for dpem_code:
Users that are interested in dpem_code are comparing it to the libraries listed below
- Differentially Private Clustering in High-Dimensional Euclidean Spaces☆12Updated 7 years ago
- Code for NIPS'2017 paper☆50Updated 4 years ago
- Differentially private release of semantic rich data☆35Updated 3 years ago
- Source code of paper "Differentially Private Generative Adversarial Network"☆69Updated 6 years ago
- A TensorFlow (Python 3) implementation of a differentially-private-GAN.☆20Updated 5 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆38Updated 6 years ago
- ☆22Updated 6 years ago
- This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"☆27Updated 2 years ago
- simple Differential Privacy in PyTorch☆48Updated 4 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆47Updated 6 years ago
- Differentially private learning on distributed data (NIPS 2017)☆12Updated 7 years ago
- ☆32Updated 7 years ago
- Salvaging Federated Learning by Local Adaptation☆56Updated 8 months ago
- Learning rate adaptation for differentially private stochastic gradient descent☆16Updated 3 years ago
- Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data☆33Updated 6 years ago
- Implementation of Minimax Pareto Fairness framework☆21Updated 4 years ago
- ☆64Updated 5 years ago
- Code for computing tight guarantees for differential privacy☆23Updated 2 years ago
- Fair Empirical Risk Minimization (FERM)☆37Updated 4 years ago
- CoLa - Decentralized Linear Learning: https://arxiv.org/abs/1808.04883☆20Updated 3 years ago
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 3 years ago
- ☆15Updated 5 years ago
- Federated Multi-Task Learning☆130Updated 6 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆131Updated 2 years ago
- Federated Principal Component Analysis Revisited!☆42Updated 3 years ago
- Privacy Risks of Securing Machine Learning Models against Adversarial Examples☆44Updated 5 years ago
- [NeurIPS 2020] Simple and practical private mean and covariance estimation.☆35Updated 4 years ago
- [IEEE S&P 22] "LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis" by Fan Wu, Yunhui Long, Ce Zhang, …☆23Updated 3 years ago
- ☆23Updated 6 years ago
- Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network☆62Updated 5 years ago