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Examining Users' Application Permissions On Android Mobile Devices

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Date Issued:
2018
Abstract/Description:
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.
Title: Examining Users' Application Permissions On Android Mobile Devices.
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Name(s): Safi, Muhammad, Author
Wisniewski, Pamela, Committee Chair
Leavens, Gary, Committee Member
Hughes, Charles, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2018
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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.
Identifier: CFE0007363 (IID), ucf:52085 (fedora)
Note(s): 2018-12-01
M.S.
Engineering and Computer Science, Computer Science
Masters
This record was generated from author submitted information.
Subject(s): Android -- Mobile -- Privacy -- Permissions -- Users -- Applications
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007363
Restrictions on Access: campus 2023-12-15
Host Institution: UCF

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