Investigating Security and Privacy Concerns in Deep-Learning-based Pervasive Health Monitoring Architectures

dc.contributor.authorBoulemtafes, Amine
dc.contributor.authorAmira, Abdelouahab
dc.contributor.authorDerki, Mohamed Saddek
dc.contributor.authorHadjar, Samir
dc.description.abstractPervasive Health Monitoring (PHM) uses sensors and wearable devices and data analytics for real-time health monitoring. It enables early detection and personalized care interventions. This technology has the potential to revolutionize healthcare by improving proactive and preventive care.Besides, Deep learning (DL) based PHM is even more promising as it improves the discovery of complex patterns and correlations. This leads to precise health monitoring and personalized care, enhances diagnostics, and ultimately improves patient outcomes in the field of healthcare.However, privacy and security considerations must be addressed for successful implementation. This paper investigates the security and privacy concerns in Pervasive Health Monitoring architectures. It discusses through an illustrative DL-based PHM architecture the potential threats and attacks during the inference and training phases, and identifies key security and privacy issues. It also gives insights on countermeasures and technological solutions that can address security and privacy concerns in PHM architectures.
dc.relation.ispartofseriesThe 5th International Conference on Pattern Analysis and Intelligent Systems; 25-26 October 2023
dc.relation.placeFerhat Abbas Sétif-1 University, Algeria
dc.structureRecherche, Filtrage et Traitement Automatique de l'Information
dc.subjectPervasive health monitoring
dc.subjectSecurity and privacy
dc.subjectDeep learning
dc.subjectPHM architectures
dc.titleInvestigating Security and Privacy Concerns in Deep-Learning-based Pervasive Health Monitoring Architectures
dc.typeConference paper