Name
Abstract | ||
|---|---|---|
Knowledge graphs (KGs) are widely shared because they can model both users' attributes as well as their relationships. Unfortunately, adversaries can re-identify their victims in these KGs by using a rich background knowledge about not only the victims' attributes but also their relationships. A preliminary work to deal with this issue has been proposed in [1] which anonymizes both user attributes and relationships, but this is not enough. Indeed, adversaries can still re-identify target users if data providers publish new versions of their anonymized KGs. We remedy this problem by presenting the k(w)-Time-Varying Attribute Degree (k(w)-tad) principle that prevents adversaries from re-identifying any user appearing in w continuous anonymized KGs with a confidence higher than 1/k. Moreover, we introduce the Cluster-based Time-Varying Knowledge Graph Anonymization Algorithm to generate anonymized KGs satisfying Finally, we prove that even if data providers insert/re-insert/update/delete their users, the users are protected by kw-lad. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICDE51399.2021.00194 | 2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021) |
Keywords | DocType | ISSN |
anonymity, privacy, dynamic knowledge graphs | Conference | 1084-4627 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anh-Tu Hoang | 1 | 0 | 0.34 |
| Barbara Carminati | 2 | 1176 | 90.25 |
| Elena Ferrari | 3 | 3782 | 510.23 |