Privacy Testing for Deep Learning

Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

Advanced Privacy-Preserving Federated Learning framework

Fast, memory-efficient, scalable optimization of deep learning with differential privacy

This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.

Federated Learning with Differential Privacy and Homomorphic Encryption.

A library for statistically estimating the privacy of ML pipelines from membership inference attacks

Similarity Guided Model Aggregation for Federated Learning

[KDD 2022] "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.

Differential Privacy Guide

A crypto-assisted framework for protecting the privacy of models and queries in inference.

Open source platform for the privacy-preserving machine learning lifecycle

Curl: Private LLMs through Wavelet-Encoded Look-Up Tables

[TOIS] "Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning"

Official implementation of FedGAT: Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction (https://arxiv.org/abs/2502.04521)

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.

Fault-tolerant secure multiparty computation in Python.

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.