opacus

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

Differential Privacy in Machine Learning under Membership Inference Attacks

A differentially private spiking neural network with temporal enhanced pooling

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

DP-SGD accuracy vs. privacy budget (ε) experiments on MNIST using Opacus — quantifies the accuracy/privacy/compute trade-off with RDP accounting.

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

Imagenet-scale DP-SGD training using Pytorch Opacus

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.

SecureMed-LLM: A privacy-preserving framework for clinical report generation from chest X-rays, integrating Med-Guard anonymization, DP-SGD (ε=3.0), adversarial training, IDS-LLM validation, and ECIES/Curve25519 encryption. PeerJ Computer Science 2025.

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.

Differentially private synthetic tabular data. T5-small with LoRA under DP-SGD, served through FastAPI and Next.js on Cloud Run.

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

Empirical study of the Privacy–Robustness–Performance trilemma in Federated Learning: combining DP-SGD, FLTrust Byzantine-robust aggregation, and Top-k compression across 8 configurations on MNIST, simulated with Flower.

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