Towards Practical Privacy-Preserving Analytics for IoT and Cloud Based Healthcare Systems

TL;DR

This paper discusses the challenges and solutions for implementing privacy-preserving analytics in IoT and cloud-based healthcare systems, focusing on balancing privacy, efficiency, and model accuracy.

Contribution

It presents an analysis of privacy requirements, existing privacy methods, and tradeoffs in developing practical privacy-preserving healthcare analytics using the kHealth system.

Findings

Identifies key privacy assets and challenges in healthcare data analytics.

Analyzes tradeoffs between privacy, efficiency, and model quality.

Provides insights into developing practical privacy-preserving solutions.

Abstract

Modern healthcare systems now rely on advanced computing methods and technologies, such as Internet of Things (IoT) devices and clouds, to collect and analyze personal health data at an unprecedented scale and depth. Patients, doctors, healthcare providers, and researchers depend on analytical models derived from such data sources to remotely monitor patients, early-diagnose diseases, and find personalized treatments and medications. However, without appropriate privacy protection, conducting data analytics becomes a source of a privacy nightmare. In this article, we present the research challenges in developing practical privacy-preserving analytics in healthcare information systems. The study is based on kHealth - a personalized digital healthcare information system that is being developed and tested for disease monitoring. We analyze the data and analytic requirements for the involved parties, identify the privacy assets, analyze existing privacy substrates, and discuss the potential tradeoff among privacy, efficiency, and model quality.