First Advisor

Wu-chi Feng

Date of Publication

Fall 1-18-2019

Document Type

Thesis

Degree Name

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

Department

Computer Science

Subjects

Data mining, Data protection, Machine learning, Computer science

DOI

10.15760/etd.6564

Physical Description

1 online resource (vii, 25 pages)

Abstract

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

https://archives.pdx.edu/ds/psu/27305

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