Accepted for/Published in: JMIR Human Factors
Date Submitted: Jun 25, 2020
Date Accepted: Jun 7, 2021
Machine Learning Based Analysis of Encrypted Medical Data In The Cloud: A Qualitative Study of Expert Stakeholders’ Perspectives
ABSTRACT
Background:
Third-party cloud-based data analysis applications are proliferating in eHealth because of the expertise offered and their monetary advantage. However, privacy and security are critical when handling sensitive medical data in the cloud. Technical advances, based on “crypto magic” in privacy-enhancing machine learning, enable data analysis in encrypted form for maintaining confidentiality. The adoption of such technologies could be counter-intuitive to relevant stakeholders in eHealth; more attention is needed on human factors for establishing trust and transparency.
Objective:
To analyze eHealth stakeholders' mental models and the perceived trade-offs in regard to data analysis on encrypted medical data in the cloud.
Methods:
In this study, we used semi-structured interviews and report on 14 interviews with medical, technical, or research expertise in eHealth.
Results:
Results show differences in understanding of, and in trusting, the technology; caution is advised by technical-experts, whereas safety-assurances are required by medical-expert. Concerns regarding the technology relate to the type of encryption applied and achieved confidentiality, quality of analysis results, data integrity and availability, transparency, and trust.
Conclusions:
Understanding risks and benefits is crucial, thus collaboration among relevant stakeholders is needed. In addition, informing clinicians and patients accordingly is important for transparency and establishing trust. Clinical Trial: none
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