Date of Award
Department or Program
David F. Kotz
Mobile health (mHealth) apps and devices are increasingly popular for health research, clinical treatment and personal wellness, as they offer the ability to continuously monitor aspects of individuals' health as they go about their everyday activities. Many believe that combining the data produced by these mHealth apps and devices may give healthcare-related service providers and researchers a more holistic view of an individual's health, increase the quality of service, and reduce operating costs. For such mHealth data to be considered useful though, data consumers need to be assured that the authenticity and the integrity of the data has remained intact---especially for data that may have been created through a series of aggregations and transformations on many input data sets. In other words, information provenance should be one of the main focuses for any system that wishes to facilitate the sharing of sensitive mHealth data. Creating such a trusted and secure data sharing ecosystem for mHealth apps and devices is difficult, however, as they are implemented with different technologies and managed by different organizations. Furthermore, many mHealth devices use ultra-low-power micro-controllers, which lack the kinds of sophisticated Memory Management Units (MMUs) required to sufficiently isolate sensitive application code and data.
In this thesis, we present an end-to-end solution for providing information provenance for mHealth data, which begins by securing mHealth data at its source: the mHealth device. To this end, we devise a memory-isolation method that combines compiler-inserted code and Memory Protection Unit (MPU) hardware to protect application code and data on ultra-low-power micro-controllers. Then we address the security of mHealth data outside of the source (e.g., data that has been uploaded to smartphone or remote-server) with our health-data system, Amanuensis, which uses Blockchain and Trusted Execution Environment (TEE) technologies to provide confidential, yet verifiable, data storage and computation for mHealth data. Finally, we look at identity privacy and data freshness issues introduced by the use of blockchain and TEEs. Namely, we present a privacy-preserving solution for blockchain transactions, and a freshness solution for data access-control lists retrieved from the blockchain.
Hardin, Taylor A., "Information Provenance for Mobile Health Data" (2022). Dartmouth College Ph.D Dissertations. 79.