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. Heikkila, 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)
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.
In this project we add differential privacy into an openset recognizer.to implement DP we use opacus library.
Implementation of my research on automating the optimal privacy budget (e) in DP-FL using epsilon-aware strategy.
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.
Colosseum-based O-RAN slice resource allocation via Federated Learning & Differential Privacy (Opacus). ClusteredFL * FedProx * DQN * PyTorch
Intrusion Detection with Differential Privacy using Opacus on the UNSW-NB15 dataset
Implement Differentially-private SGD. Large e cause low model accuracy.
Implementation of Opacus DP in Flower.
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