inference-attacks

Here are 5 public repositories matching this topic...

rezafotohi / FedAnil

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.

blockchain accuracy homomorphic-encryption privacy-preserving federated-learning privacy-preserving-machine-learning poisoning-attacks non-iid-data inference-attacks intelligent-enterprises

Updated May 26, 2024
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.

blockchain accuracy homomorphic-encryption privacy-preserving federated-learning privacy-preserving-machine-learning communication-efficiency poisoning-attacks non-iid-data inference-attacks intelligent-enterprises

Updated Sep 30, 2024
Python

rezafotohi / FedAnilPlusPlus

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FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.

blockchain accuracy homomorphic-encryption privacy-preserving federated-learning privacy-preserving-machine-learning communication-efficient poisoning-attacks non-iid-data inference-attacks intelligent-enterprises

Updated May 16, 2026
Python

ayshikakap31 / Strategic-Exposure-of-Participant-s-Data-for-Federated-Learning-based-Urban-Sensing-Applications

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This project provides a computation and communication efficient approach for federated learning based urban sensing applications against inference attacks

python3 big-data-analytics federated-learning communication-efficient privacy-preserving-computing urban-sensing geolife-dataset inference-attacks

Updated Nov 6, 2024
Jupyter Notebook

ayshikakap31 / Privacy-Preserving-and-Reliable-Federated-Learning-Framework-for-Urban-Sensing

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This project aims to develop a privacy-preserving federated learning framework to enhance data security, model reliability, and fair incentivization in urban sensing applications.

noise-maps privacy-enhancing-technologies federated-learning incentivization poisoning-attack inference-attacks

Updated Nov 1, 2024
Python

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