Framework for Visualizations of Location Privacy for Users

open_access
Open Access
Author:
Onweller, Hailey
Area of Honors:
Cybersecurity Analytics & Operations
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Anna Cinzia Squicciarini, Thesis Supervisor
Edward J Glantz, Thesis Honors Advisor
Keywords:
location privacy
Geo-Indistinguishability
privacy
user analysis
differential privacy
security
Abstract:
The applications we use are constantly collecting data about us from our social networks regarding our locations. Oftentimes we accept the terms and conditions of such applications without thinking much about how this private information can impact us in the future. Previous work on privacy has investigated how we can make datasets obfuscated using mathematical noise, known as differential privacy. This body of research has expanded to be applied to location information resulting in a new concept, referred to as Geo-Indistinguishability. Geo-Indistinguishability research has produced promising results in ensuring user privacy protection and can be very customizable because of the parameter epsilon it takes in. However, because of the mathematical nature of the application, this concept has not been widely adopted yet. Plus, individuals often do not have a good understanding of how data sharing impacts their privacy. In this paper, an application is designed to serve as a tool to help users understand (and eventually deploy) how Geo-Indistinguishability works, and to choose parameters for privacy that work for their use cases. In this paper, three user use cases are identified which are an end user, a developer and a stakeholder along with two cities, New York City and San Francisco, to explain how each would benefit from such an application to explain privacy, as well as how to use Geo-Indistinguishability. Results obtained from these use cases show that all users benefit from such an application. This application has the potential to be expanded and better implemented into everyday technologies to explain privacy, and to help users make informed decisions about the data they share.