1 research outputs found
Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy
Precision health leverages information from various sources, including omics,
lifestyle, environment, social media, medical records, and medical insurance
claims to enable personalized care, prevent and predict illness, and precise
treatments. It extensively uses sensing technologies (e.g., electronic health
monitoring devices), computations (e.g., machine learning), and communication
(e.g., interaction between the health data centers). As health data contain
sensitive private information, including the identity of patient and carer and
medical conditions of the patient, proper care is required at all times.
Leakage of these private information affects the personal life, including
bullying, high insurance premium, and loss of job due to the medical history.
Thus, the security, privacy of and trust on the information are of utmost
importance. Moreover, government legislation and ethics committees demand the
security and privacy of healthcare data. Herein, in the light of precision
health data security, privacy, ethical and regulatory requirements, finding the
best methods and techniques for the utilization of the health data, and thus
precision health is essential. In this regard, firstly, this paper explores the
regulations, ethical guidelines around the world, and domain-specific needs.
Then it presents the requirements and investigates the associated challenges.
Secondly, this paper investigates secure and privacy-preserving machine
learning methods suitable for the computation of precision health data along
with their usage in relevant health projects. Finally, it illustrates the best
available techniques for precision health data security and privacy with a
conceptual system model that enables compliance, ethics clearance, consent
management, medical innovations, and developments in the health domain.Comment: 35 pages, 3 figures, 7 table