Machine Learning-Based Analysis of Encrypted Medical Data in the Cloud: Qualitative Study of Expert Stakeholders' Perspectives.

ABSTRACT:

Background

Third-party cloud-based data analysis applications are proliferating in electronic health (eHealth) because of the expertise offered and their monetary advantage. However, privacy and security are critical concerns when handling sensitive medical data in the cloud. Technical advances based on "crypto magic" in privacy-preserving machine learning (ML) enable data analysis in encrypted form for maintaining confidentiality. Such privacy-enhancing technologies (PETs) could be counterintuitive to relevant stakeholders in eHealth, which could in turn hinder adoption; thus, more attention is needed on human factors for establishing trust and transparency.

Objective

The aim of this study was to analyze eHealth expert stakeholders' perspectives and the perceived tradeoffs

SUBMITTER: Alaqra AS 

PROVIDER: S-EPMC8485196 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature