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

TitleTowards Practical Privacy-Preserving Analytics for IoT and Cloud Based Healthcare Systems
Publication TypeJournal
Year of Publication2018
AuthorsSagar Sharma, Keke Chen, Amit Sheth
VolumePP
Issue99
Pagination1-1
Date Published01/2018
PublisherIEEE
ISSN Number1089-7801
KeywordsAnalytical models, Cloud Computing, Computational modeling, Data models, Data privacy, Medical services, Privacy
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.

Full Text

Citation:
{sharma.74,keke.chen,amit.sheth}@wright.edu
Ohio Cneter of Excellence in Knowledge-enabled Computing (Kno.e.sis),
Wright State University, Dayton

IEEE Document:
http://ieeexplore.ieee.org/document/8259427/