Redefining privacy: case study of smart health applications

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dc.contributor Atkison, Travis
dc.contributor Culmer, Nathan
dc.contributor Gray, Jeff
dc.contributor Smith, Randy
dc.contributor.advisor Carver, Jeffrey Al-Zyoud, Mahran 2020-03-12T18:06:22Z 2020-03-12T18:06:22Z 2019
dc.identifier.other u0015_0000001_0003499
dc.identifier.other AlZyoud_alatus_0004D_14052
dc.description Electronic Thesis or Dissertation
dc.description.abstract Smart health utilizes the unique capabilities of smart devices to improve healthcare. The smart devices continuously collect and transfer large amounts of useful data about the users' health. As data collection and sharing are two inevitable norms in this connected world, concerns have also been growing about the privacy of health information. Any mismatch between what the user really wants to share and what the devices share could either cause a privacy breach or limit a beneficial service. Understanding what influences information sharing can help resolve mismatches and brings protection and benefits to all stakeholders. The primary goal of this dissertation is to better understand the variability of privacy perceptions among different individuals and reflect this understanding into smart health applications. Towards this goal, this dissertation presents three studies. The first study is a systematic literature review conducted to identify the reported privacy concerns and the suggested solutions and to examine whether the context is part of any effort to describe a concern or form a solution. The study reveals 7 categories of privacy concerns and 5 categories of privacy solutions. I present a mapping between these major concerns and solutions to highlight areas in need of additional research. The results also revealed that there is a lack of both user-centric and context-aware solutions. The second study further empirically investigates the role of context and culture on the sharing decision. It describes a multicultural survey and another cross-cultural survey. The results support the intuitive view of how variable privacy perception is among different users and how understanding a user's culture could play a role in offering a smarter, dynamic set of privacy settings that reflects his privacy needs. Finally, the third study aims at providing a solution that helps users configure their privacy settings. The solution utilizes machine learning to predict the most suitable configuration for the user. As a proof of concept, I implemented and evaluated a prototype of a recommender system. Usage of such recommender systems helps make changing privacy settings less burden in addition to better reflecting the true privacy preferences of users.
dc.format.extent 259 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Computer science
dc.title Redefining privacy: case study of smart health applications
dc.type thesis
dc.type text University of Alabama. Department of Computer Science Computer Science The University of Alabama doctoral Ph.D.

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