Security and privacy issues in federated healthcare – An overview
Abstract: Securing medical records is a significant task in Healthcare communication. The major setback during the transfer of medical data in the electronic medium is the inherent difficulty in preserving data confidentiality and patients’ privacy. The innovation in technology and improvisation in the medical field has given numerous advancements in transferring the medical data with foolproof security. In today’s healthcare industry, federated network operation is gaining significance to deal with distributed network resources due to the efficient handling of privacy issues. The design of a federated security system for healthcare services is one of the intense research topics. This article highlights the importance of federated learning in healthcare. Also, the article discusses the privacy and security issues in communicating the e-health data.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Security and privacy issues in federated healthcare – An overview ; volume:12 ; number:1 ; year:2022 ; pages:57-65 ; extent:9
Open computer science ; 12, Heft 1 (2022), 57-65 (gesamt 9)
- Creator
-
Amalraj, Jansi Rani
Lourdusamy, Robert
- DOI
-
10.1515/comp-2022-0230
- URN
-
urn:nbn:de:101:1-2022072714045937073033
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:31 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Amalraj, Jansi Rani
- Lourdusamy, Robert
Other Objects (12)
An overview of implementing security and privacy in federated learning
SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems
Efficient Federated and Privacy-preserving Machine Learning
PQSF: post-quantum secure privacy-preserving federated learning
A privacy-preserving federated learning framework for blockchain networks
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Privacy-preserving federated learning based on partial low-quality data
Federated Learning with Privacy-preserving and Model IP-right-protection
Fedlabx: a practical and privacy-preserving framework for federated learning
An Abstraction-Based Approach for Privacy-Aware Federated Process Mining
COLLAGENE enables privacy-aware federated and collaborative genomic data analysis
A federated pedestrian trajectory prediction model with data privacy protection
An overview of implementing security and privacy in federated learning
SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems
Efficient Federated and Privacy-preserving Machine Learning
PQSF: post-quantum secure privacy-preserving federated learning
A privacy-preserving federated learning framework for blockchain networks
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Privacy-preserving federated learning based on partial low-quality data
Federated Learning with Privacy-preserving and Model IP-right-protection
Fedlabx: a practical and privacy-preserving framework for federated learning
An Abstraction-Based Approach for Privacy-Aware Federated Process Mining
COLLAGENE enables privacy-aware federated and collaborative genomic data analysis
A federated pedestrian trajectory prediction model with data privacy protection
An overview of implementing security and privacy in federated learning
SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems
Efficient Federated and Privacy-preserving Machine Learning
PQSF: post-quantum secure privacy-preserving federated learning
A privacy-preserving federated learning framework for blockchain networks
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Privacy-preserving federated learning based on partial low-quality data
Federated Learning with Privacy-preserving and Model IP-right-protection
Fedlabx: a practical and privacy-preserving framework for federated learning
An Abstraction-Based Approach for Privacy-Aware Federated Process Mining
COLLAGENE enables privacy-aware federated and collaborative genomic data analysis