An Industrial Grade Federated Learning Framework
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Updated
Nov 18, 2024 - Python
An Industrial Grade Federated Learning Framework
A unified framework for privacy-preserving data analysis and machine learning
Differential private machine learning
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
Get usage metrics and crash reports for your API, library, or command line tool.
Anonymizing Library for Apache Spark
pyCANON is a Python library and CLI to assess the values of the parameters associated with the most common privacy-preserving techniques.
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"
📊 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.
Differential Privacy Guide
A general-purpose privacy-preserving Analytics platform!
Sprite AI - An AI companion for your desktop
Official implementation for paper "FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition"
Code for the WWW 2021 paper - DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce
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