Secure Visual Data Processing via Federated Learning

Pedro Santos, Tânia Carvalho, Tânia Carvalho, Filipe Magalhães, Luís Antunes, Luís Antunes

2025

Abstract

As the demand for privacy in visual data management grows, safeguarding sensitive information has become a critical challenge. This paper addresses the need for privacy-preserving solutions in large-scale visual data processing by leveraging federated learning. Although there have been developments in this field, previous research has mainly focused on integrating object detection with anonymization or federated learning. However, these pairs often fail to address complex privacy concerns. On the one hand, object detection with anonymiza-tion alone can be vulnerable to reverse techniques. On the other hand, federated learning may not provide sufficient privacy guarantees. Therefore, we propose a new approach that combines object detection, federated learning and anonymization. Combining these three components aims to offer a robust privacy protection strategy by addressing different vulnerabilities in visual data. Our solution is evaluated against traditional centralized models, showing that while there is a slight trade-off in accuracy, the privacy benefits are substantial, making it well-suited for privacy sensitive applications.

Paper Citation

in Harvard Style

Santos P., Carvalho T., Magalhães F. and Antunes L. (2025). Secure Visual Data Processing via Federated Learning. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 534-541. DOI: 10.5220/0013183000003899

in Bibtex Style

@conference{icissp25,
author={Pedro Santos and Tânia Carvalho and Filipe Magalhães and Luís Antunes},
title={Secure Visual Data Processing via Federated Learning},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={534-541},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013183000003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - Secure Visual Data Processing via Federated Learning
SN - 978-989-758-735-1
AU - Santos P.
AU - Carvalho T.
AU - Magalhães F.
AU - Antunes L.
PY - 2025
SP - 534
EP - 541
DO - 10.5220/0013183000003899
PB - SciTePress