Document Type
Article
Publication Date
3-2018
Abstract
Modern healthcare systems now rely on advanced computing methods and technologies, such as IoT devices and clouds, to collect and analyze personal health data at 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 privacy nightmare. In this paper, 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.
Repository Citation
Sharma, S.,
Chen, K.,
& Sheth, A. P.
(2018). Towards Practical Privacy-Preserving Analytics for IoT and Cloud Based Healthcare Systems. IEEE Internet Computing, 22 (2), 42-51.
https://corescholar.libraries.wright.edu/knoesis/1152
DOI
10.1109/MIC.2018.112102519
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
The article posted is the postprint version.