De-anonymizing Social Network Neighborhoods Using Auxiliary and Semantic Information
Morgan, Steven Michael
Novillo, Jorge; Adviser
Andriamanalimanana, Bruno; Reviewer
Reale, Michael; Reviewer
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Subjectsocial networks; semantic information; data mining; auxiliary information; textual analysis; social network data
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.
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.