Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 11, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
Trains a differentially-private linear regression inside of the RISC-Zero virtual machine.
机器学习和差分隐私的论文笔记和代码仓
Hybrid neural network is protected against adversarial attacks using various defense techniques, including input transformation, randomization, and adversarial training.
Collection of tools and resources for managing the statistical disclosure control of trained machine learning models
Contains interesting projects like Cat face detection, cat face recognition, code generation, Building chatbot, finding similar documents, image segmentation, UCI credit card, anomaly detection, MNIST etc.
The core library of differential privacy algorithms powering the OpenDP Project.
Differentially Private Selection using Smooth Sensitivity
Privacy-Optimized Randomized Response for Sharing Multi-Attribute Data
We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study
Google's differential privacy libraries.
Bias evaluation of Differentially Private NLP models
Generating tabular datasets under differential privacy
A unified framework for privacy-preserving data analysis and machine learning
Simulation framework for accelerating research in Private Federated Learning
A Python Package for NLP obfuscation using Differential Privacy
Cross-silo Federated Learning playground in Python. Discover 7 real-world federated datasets to test your new FL strategies and try to beat the leaderboard.
Fast, memory-efficient, scalable optimization of deep learning with differential privacy
An open-source implementation of PrivSyn: Differentially Private Data Synthesis (USENIX Security Conference, 21)
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