Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers
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Updated
May 27, 2024 - Python
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers
Curl: Private LLMs through Wavelet-Encoded Look-Up Tables
Extremely Randomized Trees with Privacy Preservation for Distributed Data (k-PPD-ERT)
Similarity Guided Model Aggregation for Federated Learning
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.
Implementation of the Heflp, a framework enabling practical and overflow-safe federated learning.
Python Privacy framework
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.
(in development) Home assistant custom component aiming to help self-consumers optimize their energy use in local and private manner.
Hands-on part of the Federated Learning and Privacy-Preserving ML tutorial given at VISUM 2022
Differential Privacy Guide
Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks
Fault-tolerant secure multiparty computation in Python.
Open source platform for the privacy-preserving machine learning lifecycle
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A crypto-assisted framework for protecting the privacy of models and queries in inference.
📊 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.
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data
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