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Title:Privacy risk and de-anonymization in heterogeneous information networks
Author(s):Zhang, Aston
Advisor(s):Gunter, Carl A.; Han, Jiawei
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Privacy, Information Networks
Abstract:Anonymized user datasets are often released for research or industry applications. As an example, released its anonymized users’ profile, social interaction, and recommendation log data in KDD Cup 2012 to call for recommendation algorithms. Since the entities (users and so on) and edges (links among entities) are of multiple types, the released social network is a heterogeneous information network. Prior work has shown how privacy can be compromised in homogeneous information networks by the use of specific types of graph patterns. We show how the extra information derived from heterogeneity can be used to relax these assumptions. To characterize and demonstrate this added threat, we formally define privacy risk in an anonymized heterogeneous information network to identify the vulnerability in the possible way such data are released, and further present a new de-anonymization attack that exploits the vulnerability. Our attack successfully de-anonymized most individuals involved in the data. We further show that the general ideas of exploiting privacy risk and de-anonymizing heterogeneous information networks can be extended to more general graphs.
Issue Date:2015-07-22
Rights Information:Copyright 2015 Aston Zhang
Date Available in IDEALS:2016-09-09
Date Deposited:2015-08

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