Protecting Patient Privacy with Encrypted Federated Learning Models

Conference paper

Authors

Mounira Tarhouni
Fedwa Mansouri
Bechir Alaya
Salah Zidi

Research overview

This conference paper examines federated learning with homomorphic encryption for medical-data privacy. It addresses how collaborative model training can be studied alongside the protection of sensitive data.

Publication details

Journal / proceedings
2023 IEEE Third International Conference on Signal, Control and Communication (SCC)
Publisher
IEEE

Related expertise

Citation

Mounira Tarhouni; Fedwa Mansouri; Bechir Alaya; Salah Zidi. Protecting Patient Privacy with Encrypted Federated Learning Models. 2023 IEEE Third International Conference on Signal, Control and Communication (SCC). https://ieeexplore.ieee.org/document/10527615/

Publication by a BECOME Researcher

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Scientist

becomePhD

Fedwa MANSOURI

PhD Researcher | AI & NLP

Fedwa Mansouri is a doctoral researcher in Computer Science at the University of Gabès and a member of BECOME's scientific team through becomePhD. Her interests span language models, NLP, Deep Learning and Reinforcement Learning, supported by applied experience in text classification and Python development.

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