Publication Date
8-2023
Date of Final Oral Examination (Defense)
4-14-2023
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Computer Science
Department
Computer Science
Supervisory Committee Chair
Hoda Mehrpouyan, Ph.D.
Supervisory Committee Member
Michael Ekstrand, Ph.D.
Supervisory Committee Member
Jaclyn Kettler, Ph.D.
Supervisory Committee Member
Amit Jain, Ph.D.
Abstract
Risk is something that surrounds us each and every day, and learning how to manage risk in different areas is necessary to limit its impact. Two different areas of risk have been identified for this thesis: election day incident infrastructure and user privacy disclosure prevention. We ask if it is possible to leverage information related to risk to create procedures that handle it as an overall issue in order to apply procedures to similar areas of research. Understanding how to identify and prevent these potential areas of risk is important to secure information not just for a single person, but potentially for whole populations. Developing plans of action to prevent severe problems within these areas may improve not only election day incident communication but also personal trust in technology to share private information accurately.
This thesis proposes interdisciplinary techniques through qualitative analysis, quantitative analysis, and machine learning tools to build solution frameworks for these unique challenges. In Idaho, communication between election officials is currently broken when dealing with problems on election day. Many times, officials use multiple streams of communication (i.e., text, calls, emails, etc.) to relay information to one another. This fractured process risks the inability to track the progress made on incidents, the potential inefficiency with resolution, and information loss between different communication streams. Therefore, we interviewed election officials from multiple states to assess the need for a centralized incident management and communication tool to assist in incidents. The security of personal information is an important part of the identity of a person or organization. If private information is leaked unintentionally to incorrect audiences, it could reflect poorly on the person or organization. Therefore, we analyze how to prevent the risk of undesirable privacy disclosure using unsupervised and transfer learning techniques to leverage public and private information for prediction.
The results pointed favorably to the implementation of a communication tool for election administrators to assist with efficient communication and response to election day problems. Learning outcomes and best practices for preventing unwarranted private information disclosures to users will also be presented. Further analysis is recommended to implement these contributions in real-world environments.
DOI
https://doi.org/10.18122/td.2124.boisestate
Recommended Citation
Parker, Kamryn DeAnn, "Risk Assessment and Solutions for Two Domains: Election Procedures and Privacy Disclosure Prevention for Users" (2023). Boise State University Theses and Dissertations. 2124.
https://doi.org/10.18122/td.2124.boisestate