Privacy-preserving deep learning for pervasive health monitoring: a study of environment requirements and existing solutions adequacy

Autor: Boulemtafes, Amine, Derhab, Abdelouahid, Challal, Yacine
Rok vydání: 2022
Předmět:
Zdroj: Health and Technology
ISSN: 2190-7196
2190-7188
DOI: 10.1007/s12553-022-00640-3
Popis: In recent years, deep learning in healthcare applications has attracted considerable attention from research community. They are deployed on powerful cloud infrastructures to process big health data. However, privacy issue arises when sensitive data are offloaded to the remote cloud. In this paper, we focus on pervasive health monitoring applications that allow anywhere and anytime monitoring of patients, such as heart diseases diagnosis, sleep apnea detection, and more recently, early detection of Covid-19. As pervasive health monitoring applications generally operate on constrained client-side environment, it is important to take into consideration these constraints when designing privacy-preserving solutions. This paper aims therefore to review the adequacy of existing privacy-preserving solutions for deep learning in pervasive health monitoring environment. To this end, we identify the privacy-preserving learning scenarios and their corresponding tasks and requirements. Furthermore, we define the evaluation criteria of the reviewed solutions, we discuss them, and highlight open issues for future research.
Databáze: OpenAIRE
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