Collection of tools and resources for managing the statistical disclosure control of trained machine learning models
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Updated
May 30, 2024 - Python
Collection of tools and resources for managing the statistical disclosure control of trained machine learning models
A curated list of advancements in Vertical Federated Learning, frameworks and libraries.
Simulation framework for accelerating research in Private Federated Learning
Differentially Private Selection using Smooth Sensitivity
Fit interpretable models. Explain blackbox machine learning.
Training PyTorch models with differential privacy
The core library of differential privacy algorithms powering the OpenDP Project.
Privacy-Preserving Publication of GWAS Statistics using Smooth Sensitivity
A Comparative Study of Gradient Clipping Techniques in DP-SGD
机器学习和差分隐私的论文笔记和代码仓
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers
A library to generate synthetic tabular or RDF data using Conditional Generative Adversary Networks (GANs) combined with Differential Privacy techniques.
A unified framework for privacy-preserving data analysis and machine learning
Interact with my projects to quickly assess their value and relevance to your academic or business needs.
We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study
Differential Privacy (DP) is a technique for preserving the privacy of individuals in a dataset while allowing meaningful analysis of the data. The idea of the technique is to add random noise to the data in such a way that no inferences can be made about sensitive data.
A Python Package for NLP obfuscation using Differential Privacy
Hybrid neural network is protected against adversarial attacks using various defense techniques, including input transformation, randomization, and adversarial training.
Diffprivlib: The IBM Differential Privacy Library
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