Privacy Risk in Anonymized Heterogeneous Information Networks

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Provided by: Creative Commons
Topic: Big Data
Format: PDF
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 in-formation network. Prior work has shown how privacy can be com-promised in homogeneous information networks by the use of specific types of graph patterns. They show how the extra information derived from heterogeneity can be used to relax these assumptions.
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