Publications tagged with Health care
Published:
Publications tagged with "Health care"
- DetailsBarzegar, A., Campanile, L., Marrone, S., Marulli, F., Verde, L., & Mastroianni, M. (2024). Fuzzy-based Severity Evaluation in Privacy Problems: An Application to Healthcare [Conference paper]. Proceedings - 2024 19th European Dependable Computing Conference, EDCC 2024, 147–154. https://doi.org/10.1109/EDCC61798.2024.00037
Abstract
The growing diffusion of smart pervasive applications is starting to mine personal privacy: from Internet of Things to Machine Learning, the opportunities for privacy loss are many. As for other concerns involving people and goods as financial, safety and security, researchers and practitioners have defined in time different risk assessment procedures to have repeatable and accurate ways of detecting, quantifying and managing the (possible) source of privacy loss. This paper defines a methodology to deal with privacy risk assessment, overcoming the traditional dichotomy between qualitative (easy to apply) and quantitative (accurate) approaches. The present paper introduces an approach based on fuzzy logic, able to conjugate the benefits of both techniques. The feasibility of the proposed methodology is demonstrated using a healthcare case study. © 2024 IEEE. - DetailsCampanile, L., Marrone, S., Marulli, F., & Verde, L. (2022). Challenges and Trends in Federated Learning for Well-being and Healthcare [Conference paper]. Procedia Computer Science, 207, 1144–1153. https://doi.org/10.1016/j.procs.2022.09.170
Abstract
Currently, research in Artificial Intelligence, both in Machine Learning and Deep Learning, paves the way for promising innovations in several areas. In healthcare, especially, where large amounts of quantitative and qualitative data are transferred to support studies and early diagnosis and monitoring of any diseases, potential security and privacy issues cannot be underestimated. Federated learning is an approach where privacy issues related to sensitive data management can be significantly reduced, due to the possibility to train algorithms without exchanging data. The main idea behind this approach is that learning models can be trained in a distributed way, where multiple devices or servers with decentralized data samples can provide their contributions without having to exchange their local data. Recent studies provided evidence that prototypes trained by adopting Federated Learning strategies are able to achieve reliable performance, thus by generating robust models without sharing data and, consequently, limiting the impact on security and privacy. This work propose a literature overview of Federated Learning approaches and systems, focusing on its application for healthcare. The main challenges, implications, issues and potentials of this approach in the healthcare are outlined. © 2022 The Authors. Published by Elsevier B.V.