  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.
