Privacy Risk in Anonymized Heterogeneous Information Networks

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.

Provided by: Creative Commons Topic: Big Data Date Added: Mar 2014 Format: PDF

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