Time-Aware Anonymization of Knowledge Graphs

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