Website for Privacy Engineering Program at CMU
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Updated
Jun 4, 2024 - HTML
Website for Privacy Engineering Program at CMU
A curated list of awesome responsible machine learning resources.
Your GoTo Library for NN's over MPC
An open framework for Federated Learning.
Fast, memory-efficient, scalable optimization of deep learning with differential privacy
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Training PyTorch models with differential privacy
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
Advanced Privacy-Preserving Federated Learning framework
Privacy-Preserving Machine Learning (PPML) Tutorial
A Comparative Study of Gradient Clipping Techniques in DP-SGD
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers
FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.
FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.
Birhanu Eshete is an Associate Professor of Computer Science at the University of Michigan, Dearborn. His main research focus is in trustworthy machine learning with emphasis on security, safety, privacy, interpretability, fairness, and the dynamics thereof. He also studies online cybercrime and advanced and persistent threats (APTs).
Fault-tolerant secure multiparty computation in Python.
Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.
Extremely Randomized Trees with Privacy Preservation for Distributed Data (k-PPD-ERT)
A library for statistically estimating the privacy of ML pipelines from membership inference attacks
(in development) Home assistant custom component aiming to help self-consumers optimize their energy use in local and private manner.
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