First Advisor

Wu-chi Feng

Date of Publication

Fall 1-18-2019

Document Type


Degree Name

Master of Science (M.S.) in Computer Science


Computer Science


Data mining, Data protection, Machine learning, Computer science



Physical Description

1 online resource (vii, 25 pages)


Contemporary computers attempt to understand a user's actions and preferences in order to make decisions that better serve the user. In pursuit of this goal, computers can make observations that range from simple pattern recognition to listening in on conversations without the device being intentionally active. While these developments are incredibly useful for customization, the inherent security risks involving personal data are not always worth it. This thesis attempts to tackle one issue in this domain, computer usage identification, and presents a solution that identifies high-level usage of a system at any given moment without looking into any personal data. This solution, what I call "knowing without knowing," gives the computer just enough information to better serve the user without knowing any data that compromises privacy. With prediction accuracy at 99% and system overhead below 0.5%, this solution is not only reliable but is also scalable, giving valuable information that will lead to newer, less invasive solutions in the future.

Persistent Identifier