McGill-DMaS / Privacy-DiffGenLinks
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
☆17Updated 9 years ago
Alternatives and similar repositories for Privacy-DiffGen
Users that are interested in Privacy-DiffGen are comparing it to the libraries listed below
Sorting:
- Implementation of the peer-to-peer simulation used for the experimental evaluation of the Heterogeneous Differential Privacy paper.☆10Updated 5 years ago
- This work combines differential privacy and multi-party computation protocol to achieve distributed machine learning.☆26Updated 4 years ago
- A Privacy Preserving Data Mining Platform☆46Updated 13 years ago
- Model extraction attacks on Machine-Learning-as-a-Service platforms.☆349Updated 4 years ago
- ☆55Updated 5 years ago
- [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples☆67Updated last month
- Naive implementation of basic Differential-Privacy framework and algorithms☆48Updated 2 years ago
- Implementation of membership inference and model inversion attacks, extracting training data information from an ML model. Benchmarking …☆103Updated 5 years ago
- Secure collaborative training and inference for XGBoost.☆105Updated 2 years ago
- Game-Theoretic Adversarial Machine Learning Library☆59Updated 6 years ago
- ☆38Updated 3 years ago
- Code for the IEEE S&P 2018 paper 'Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning'☆53Updated 4 years ago
- Federated Learning on XGBoost☆46Updated 5 years ago
- A library for adversarial classifier evasion☆43Updated 10 years ago
- Python Implementation for Mondrian Multidimensional K-Anonymity (Mondrian).☆174Updated last year
- CaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where b…☆26Updated 3 years ago
- A compiled list of resources and materials for PPML☆11Updated 3 months ago
- Core streaming heterogeneous graph clustering and anomaly detection code (KDD 2016)☆40Updated 5 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆135Updated 2 years ago
- Privacy-preserving Deep Learning based on homomorphic encryption (HE)☆34Updated 3 years ago
- [NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contrib…☆27Updated 6 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆54Updated 6 years ago
- Code for Machine Learning Models that Remember Too Much (in CCS 2017)☆30Updated 7 years ago
- Federated gradient boosted decision tree learning☆68Updated 2 years ago
- privacy preserving deep learning☆15Updated 7 years ago
- ☆32Updated 7 years ago
- A TensorFlow (Python 3) implementation of a differentially-private-GAN.☆20Updated 5 years ago
- Trojan Attack on Neural Network☆184Updated 3 years ago
- Python package to create adversarial agents for membership inference attacks againts machine learning models☆47Updated 6 years ago
- This repository contains the codes for first large-scale investigation of Differentially Private Convex Optimization algorithms.☆63Updated 6 years ago