Privacy Testing for Deep Learning
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Updated
Jul 20, 2023 - Python
Privacy Testing for Deep Learning
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
Fast, memory-efficient, scalable optimization of deep learning with differential privacy
Advanced Privacy-Preserving Federated Learning framework
This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.
Privacy-Preserving Federated Learning Applied to Decentralized Data
A library for statistically estimating the privacy of ML pipelines from membership inference attacks
Similarity Guided Model Aggregation for Federated Learning
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
📊 Privacy Preserving Medical Data Analytics using Secure Multi Party Computation. An End-To-End Use Case. A. Giannopoulos, D. Mouris M.Sc. thesis at the University of Athens, Greece.
A crypto-assisted framework for protecting the privacy of models and queries in inference.
Open source platform for the privacy-preserving machine learning lifecycle
Differential Privacy Guide
Curl: Private LLMs through Wavelet-Encoded Look-Up Tables
Fault-tolerant secure multiparty computation in Python.
Hands-on part of the Federated Learning and Privacy-Preserving ML tutorial given at VISUM 2022
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.
Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks
FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.
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