An Industrial Grade Federated Learning Framework
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Updated
Nov 19, 2024 - Python
An Industrial Grade Federated Learning Framework
A unified framework for privacy-preserving data analysis and machine learning
Differential private machine learning
My Privacy DNS #Matrix lists for blacklisting
A multiple parties joint, distributed execution engine based on Ray, to help build your own federated learning frameworks in minutes.
(SIGCOMM '22) Practical GAN-based Synthetic IP Header Trace Generation using NetShare
Privacy -preserving Neural Networks
Python implementation of anonymous linkage using cryptographic linkage keys
personal implementation of secure aggregation protocol
pyCANON is a Python library and CLI to assess the values of the parameters associated with the most common privacy-preserving techniques.
Get usage metrics and crash reports for your API, library, or command line tool.
Anonymizing Library for Apache Spark
This is a proof-of-concept implementation of the framework proposed by [Alves and Aranha 2018] with the purpose of offering a wrapper on MongoDB's Python driver that enables a application to store and query encrypted data on the database.
Inference demo and evaluation scripts for the MICCAI-2019 paper "Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images"
Differential Privacy Guide
📊 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.
Sprite AI - An AI companion for your desktop
A general-purpose privacy-preserving Analytics platform!
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.
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