Wearable devices create exciting opportunities for home monitoring of chronic/elderly patients and reduce the length/cost of hospitalization. However, the very same devices have weak security and scatter sensitive data. We envision that via advanced wireless technologies, wearable health monitoring devices can transmit data directly to a healthcare service provider (cloud), and receive commands from the cloud for device authentication, privacy protection and personalized healthcare treatments. The system can be very simple in its architecture, yet extremely powerful in its functionality. This project shall study the architecture and data analytics for such a system and focus on: (i) employing statistical methods and machine learning techniques to the collected data to extract its medical meaning; (ii) investigate efficient and privacy-preserving: authentication protocols and verifiable outsourcing/delegation of computations to the cloud. We shall approach this problem from a cross-disciplinary perspective and use advances from cryptography, and machine learning. We will employ a prototype system, called Cloud Sense for Health, to gather substantial healthcare data to support research in medicine and statistical learning. The partners of this project are: Chalmers University of Technology and Harvard University. The project is funded by STINT.
at Computer Science and Engineering, Networks and Systems (Chalmers)
Funding years 2015–2016
Area of Advance