Abstract

Mobile devices have become one of the most important computing platforms. The platform's portability and highly customized nature raises several privacy concerns. Therefore, understanding and predicting user privacy behavior has become very important if one is to design software which respects the privacy concerns of users. Various studies have been carried out to quantify user perceptions and concerns [23,36] and user characteristics which may predict privacy behavior [21,22,25]. Even though significant research exists regarding factors which affect user privacy behavior, there is gap in the literature when it comes to correlating these factors to objectively collected data from user devices. We designed an Android application which administered surveys to collect various perceived measures, and to scrape past behavioral data from the phone. Our goal was to discover variables which help in predicting user location sharing decisions by correlating what we collected from surveys with the user's decision to share their location with our study application. We carried out logistic regression analysis with multiple measured variables and found that perceived measures and past behavioral data alone were poor predictors of user location sharing decisions. Instead, we discovered that perceived measures in the context of past behavior helped strengthen prediction models. Asking users to reflect on whether they were comfortable sharing their location with apps that were already installed on their mobile device was a stronger predictor of location sharing behavior than general measures regarding privacy concern or past behavioral data scraped from their phones. This work contributes to the field by correlating existing privacy measures with objective data, and uncovering a new predictor of location sharing decisions.

Graduation Date

2018

Semester

Fall

Advisor

Wisniewski, Pamela

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0007363

URL

http://purl.fcla.edu/fcla/etd/CFE0007363

Language

English

Release Date

December 2023

Length of Campus-only Access

5 years

Access Status

Masters Thesis (Campus-only Access)

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