Collection of tools and resources for managing the statistical disclosure control of trained machine learning models
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Updated
Jun 10, 2024 - Python
Collection of tools and resources for managing the statistical disclosure control of trained machine learning models
Fit interpretable models. Explain blackbox machine learning.
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.
A Comparative Study of Gradient Clipping Techniques in DP-SGD
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)
Differential Privacy (DP) is a technique for preserving the privacy of individuals in a dataset while allowing meaningful analysis of the data. This technique adds random noise to the data in a way such that no inferences can be made about sensitive data.
A principled library for tuning, training and evaluating tabular data synthesis on fidelity, privacy and utility.
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