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dc.contributor.authorMorgan, Steven Michael
dc.contributor.authorNovillo, Jorge; Adviser
dc.contributor.authorAndriamanalimanana, Bruno; Reviewer
dc.contributor.authorReale, Michael; Reviewer
dc.date.accessioned2016-06-21T20:10:48Z
dc.date.accessioned2020-06-22T14:35:20Z
dc.date.available2016-06-21T20:10:48Z
dc.date.available2020-06-22T14:35:20Z
dc.date.issued2015-12-11
dc.identifier.urihttp://hdl.handle.net/20.500.12648/1091
dc.descriptionApproved and recommended for acceptance as a thesis in partial fulfillment of the requirements for the degree of Master of Science in Computer and Information Science.en_US
dc.description.abstractThe increasing popularity of social networks and their progressively more robust uses provides an interesting intersection of data. Social graphs have been rigorously studied for de-anonymization. Users of social networks will provide feedback to pages of interest and will create a vibrant profile. In addition to user interests, textual analysis provides another feature set for users. The user profile can be viewed as a classical relational dataset in conjunction with graph data. This paper uses semantic information to improve the accuracy of de-anonymizing social network data.en_US
dc.language.isoen_USen_US
dc.subjectsocial networksen_US
dc.subjectsemantic informationen_US
dc.subjectdata miningen_US
dc.subjectauxiliary informationen_US
dc.subjecttextual analysisen_US
dc.subjectsocial network dataen_US
dc.titleDe-anonymizing Social Network Neighborhoods Using Auxiliary and Semantic Informationen_US
dc.typeThesisen_US
refterms.dateFOA2020-06-22T14:35:20Z
dc.description.institutionSUNY Polytechnic Institute


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    This collection contains master's theses, capstone projects, and other student and faculty work from programs within the Department of Engineering, including computer science and network security.

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