McGill-DMaS / Privacy-DiffGen
Differentially private data release for data mining [SIGKDD 2011] - convert a relational data set into a differentially-private version while maintaining its capability for data mining
☆16Updated 9 years ago
Related projects ⓘ
Alternatives and complementary repositories for Privacy-DiffGen
- A Privacy Preserving Data Mining Platform☆46Updated 12 years ago
- Implementation of the peer-to-peer simulation used for the experimental evaluation of the Heterogeneous Differential Privacy paper.☆10Updated 4 years ago
- ☆23Updated 5 years ago
- ☆10Updated last year
- This work combines differential privacy and multi-party computation protocol to achieve distributed machine learning.☆26Updated 4 years ago
- Naive implementation of basic Differential-Privacy framework and algorithms☆47Updated 2 years ago
- ☆43Updated 3 years ago
- WAFFLE: Watermarking in Federated Learning☆15Updated last year
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆38Updated 5 years ago
- privacy preserving deep learning☆15Updated 7 years ago
- Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…☆41Updated 2 years ago
- Differentially Private Conditional Generative Adversarial Network☆30Updated 3 years ago
- A general method for training cost-sensitive robust classifier☆21Updated 5 years ago
- Implementation of "Machine Learning Classification over Encrypted Data" by Raphael Bost, Raluca Ada Popa, Stephen Tu and Shafi Goldwasser☆38Updated 7 years ago
- Code for Machine Learning Models that Remember Too Much (in CCS 2017)☆30Updated 7 years ago
- DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation☆16Updated 4 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆53Updated 5 years ago
- Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data☆33Updated 5 years ago
- ☆23Updated 10 months ago
- A paper summary of Backdoor Attack against Neural Network☆13Updated 5 years ago
- ☆38Updated 2 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆31Updated 3 years ago
- Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy☆17Updated last month
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆129Updated last year
- Machine Unlearning for Random Forests☆17Updated 4 months ago
- Implementation of membership inference and model inversion attacks, extracting training data information from an ML model. Benchmarking …☆99Updated 5 years ago
- Code for the paper "Bayesian Differential Privacy for Machine Learning"☆22Updated 4 years ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆25Updated 3 months ago
- Differentially private release of semantic rich data☆35Updated 3 years ago