Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)

Easy-to-use utilities to build privacy-preserving AI.

Securing Collaborative Medical AI by Using Differential Privacy

Code for the paper "PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning" by L. Corbucci, M. A. Heikkilä, D.S. Noguero, A. Monreale, N. Kourtellis.

Hands-on part of the Federated Learning and Privacy-Preserving ML tutorial given at VISUM 2022

Building an AI model for chest X-ray under patient privacy guarantees

A differentially private spiking neural network with temporal enhanced pooling

A Comparative Study of Gradient Clipping Techniques in Differentially Private Stochastic Gradient Descent (DP-SGD)

Companion repository for the paper Threat-Driven Frameworks for Privacy-Preserving Machine Learning: A Practitioner’s Guide (2017–2025). Contains benchmarking tables, framework metrics, figure sources, and reference summaries for privacy-preserving ML techniques.

A hands-on educational walkthrough of training a CelebA (Eyeglasses) image classifier with Differentially Private SGD using PyTorch and Opacus. The focus of this repo is on clarity and reproducibility through balanced subsets, deterministic preprocessing, and side-by-side baseline vs. DP training, while acknowledging real trade-offs.

Imagenet-scale DP-SGD training using Pytorch Opacus

Federated learning system for image classification with gradient privacy protection and gradient inversion attack simulation.

In this project we add differential privacy into an openset recognizer.to implement DP we use opacus library.

Privacy-preserving credit risk scoring API: DP-SGD + API hardening defending against membership inference and model extraction (307K records, <1% AUC drop at ε=2.0)

Colosseum-based O-RAN slice resource allocation via Federated Learning & Differential Privacy (Opacus). ClusteredFL · FedProx · DQN · PyTorch

Reproducible benchmark comparing centralized, federated, and differentially private federated learning for cardiovascular disease prediction.

Implementation of Opacus DP in Flower.

Implement Differentially-private SGD. Large ε cause low model accuracy.

Implementation of my research on automating the optimal privacy budget (ε) in DP-FL using epsilon-aware strategy.

[Tutorials] Privacy Preserving MedMNIST Benchmarking.