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Restoring Information Needed for Social Internetworking Analysis from Anonymized Data

Authors:
Francesco Buccafurri
Daniele Caridi
Gianluca Lax
Antonino Nocera
Domenico Ursino

Keywords: Social Network; Social Network Analysis; Social Internetworking System; Anonymized Data; Clustering

Abstract:
The interaction among distinct social networks is the basis of a new emergent internetworking scenario (called Social Internetworking Scenario or, simply, SIS), enabling a lot of strategic applications whose main strength will be just the integration of possibly different communities yet preserving their diversity and autonomy. As a consequence, studying this new scenario from a Social-Network-Analysis perspective is certainly an important and topical issue, also for the possibility of discovering a lot of relevant knowledge about multiple aspects of people life. However, not always the analyst is able to deal with the hard problem of collecting data through the execution of a crawler. In this case, she could exploit graph-based social data, collected by another party, and usually anonymized for privacy reasons. Unfortunately, even the most frequent and trivial anonymization (i.e., the elimination of URLs associated to nodes), handicaps a lot of SIS-oriented investigations, due to the lack of some relevant explicit information. In this paper, we deal with this problem, by proposing and by experimentally validating a clustering-based technique able to restore part of the missing explicit information, thus allowing the profitable analysis of anonymized multi-social-network data.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2013

Publication date: July 21, 2013

Published in: conference

ISSN: 2308-4529

ISBN: 978-1-61208-283-7

Location: Nice, France

Dates: from July 21, 2013 to July 26, 2013