Health Modeling Using Smart Device Data; Physiological Data Privacy in the Digital Age 273 views

Due to the severity of the COVID-19 crisis, governments have been scrambling to find any means to slow down or prevent any spreading of the disease. They have used various methods to hold back the disease such as quarantining, limiting travel, and even using apps for contact tracing and detecting the illness. This contact tracing is accomplished by collecting physiological data from mobile phones and wearable devices. Anybody who uses their smartphone or wears a “smart” device is subject to this collection of data. This data is collected by large companies employed by governments to supposedly detect signs of the illness. However, the collection of this data can violate a person’s privacy or be used malevolently. The use of smart devices to monitor the spread of COVID-19 is a pertinent example that correlates the topic of physiological data privacy with the use of smart device data for health modeling. The STS study explored the concept of privacy in relation to physiological data collection. As wearable devices become more common, the topic of physiological data collection will become more crucial. The topic was explored through the use of surveys, interviews, and document review. The technical study connects to the STS topic by exploring how personal health data collected from smart devices can be used. One of the supposed benefits of collecting personal health data is that it can be utilized to detect mental and physical illnesses in users. The study explored this idea and utilized machine learning to glean useful information from a user’s personal health data. The study also explored what data was available from the smart devices and the amount of personal data that was present in the data.

BS (Bachelor of Science)

Data privacy; Smart devices; Modeling

School of Engineering and Applied Science Bachelor of Science in Computer Science Technical Advisor: Afsaneh Doryab STS Advisor: Tsai-Hsuan Ku Technical Team Members: Keshav Ailaney, Wei Wang, Johan Ketkar

English

All rights reserved (no additional license for public reuse)

2021-05-16