Time-Aware Anonymization of Knowledge Graphs

Abstract

Knowledge graphs (KGs) play an essential role in data sharing because they can model both users’ attributes and their relationships. KGs can tailor many data analyses, such as classification where a sensitive attribute is selected and the analyst analyzes the associations between users and the sensitive attribute’s values (aka sensitive values). Data providers anonymize their KGs and share the anonymized versions to protect users’ privacy. Unfortunately, an adversary can exploit these attributes and relationships to infer sensitive information by monitoring either one or many snapshots of a KG. To cope with this issue, in this paper, we introduce (k, l)-Sequential Attribute Degree ((k, l)-sad), an extension of the k^w-tad principle[10], to ensure that sensitive values of re-identified users are diverse enough to prevent them from being inferred with a confidence higher than \(\frac{1}{l} \) even though adversaries monitor all published KGs. In addition, we develop the Time-Aware Knowledge Graph Anonymization Algorithm to anonymize KGs such that all published anonymized versions of a KG satisfy the (k, l)-sad principle, by, at the same time, preserving the utility of the anonymized data. We conduct experiments on four real-life datasets to show the effectiveness of our proposal and compare it with k^w-tad.