Specialization in the identity ecosystem

dc.contributor.advisorBarber, K. Suzanne
dc.creatorZhu, Liang, active 21st centuryen
dc.date.accessioned2015-03-09T15:57:36Zen
dc.date.issued2014-12en
dc.date.submittedDecember 2014en
dc.date.updated2015-03-09T15:57:36Zen
dc.descriptiontexten
dc.description.abstractCyberspace has dramatically improved our daily lives in the past several decades. Meanwhile, people’s personal identifiable information (PII) is exposed online and is at risk of identity theft and cybercrimes. The Identity Ecosystem developed by the Center for Identity in the University of Texas at Austin addresses this problem and provides a statistical framework for understanding the value, risk and mutual relationships of PII. The Identity Ecosystem currently uses a general Bayesian Network Model to simulate the relationships among PII, which may be quite inaccurate for specific groups of people. This thesis proposes a solution that specializes the Bayesian Network used for particular groups of people. Both one-dimension specialization and multi-dimension specialization are investigated. Research problems like how to choose specialization criterion, how to set specialization boundaries, and how to overcome the difficult of insufficient data, are carefully studied. Specialization functionality is demonstrated based on empirical data. Finally, experiments of specialization are conducted on data obtained from online stories. This work is important in the sense that it provides a guide-line of designing more accurate models of PII within the Identity Ecosystem.en
dc.description.departmentElectrical and Computer Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/29085en
dc.language.isoenen
dc.subjectSpecializationen
dc.subjectGraphic modelen
dc.subjectIdentity safetyen
dc.titleSpecialization in the identity ecosystemen
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

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