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De-anonymizing Social Network Neighborhoods Using Auxiliary and Semantic Information

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social networks
 semantic information
 data mining
 auxiliary information
 textual analysis
 social network data
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2015-12-11
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SUNY Polytechnic Institute College of Engineering
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Steven Morgan Thesis
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http://hdl.handle.net/20.500.12648/1091
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.
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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.
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