De-anonymizing Social Network Neighborhoods Using Auxiliary and Semantic Information
dc.contributor.author | Morgan, Steven Michael | |
dc.contributor.author | Novillo, Jorge; Adviser | |
dc.contributor.author | Andriamanalimanana, Bruno; Reviewer | |
dc.contributor.author | Reale, Michael; Reviewer | |
dc.date.accessioned | 2016-06-21T20:10:48Z | |
dc.date.accessioned | 2020-06-22T14:35:20Z | |
dc.date.available | 2016-06-21T20:10:48Z | |
dc.date.available | 2020-06-22T14:35:20Z | |
dc.date.issued | 2015-12-11 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12648/1091 | |
dc.description | Approved 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.abstract | The 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.iso | en_US | en_US |
dc.subject | social networks | en_US |
dc.subject | semantic information | en_US |
dc.subject | data mining | en_US |
dc.subject | auxiliary information | en_US |
dc.subject | textual analysis | en_US |
dc.subject | social network data | en_US |
dc.title | De-anonymizing Social Network Neighborhoods Using Auxiliary and Semantic Information | en_US |
dc.type | Thesis | en_US |
refterms.dateFOA | 2020-06-22T14:35:20Z | |
dc.description.institution | SUNY Polytechnic Institute |
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SUNY Polytechnic Institute College of Engineering
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.