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- W3176806483 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 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>w</sup> -Time-Varying Attribute Degree (k <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>w</sup> -tad) principle that prevents adversaries from re-identifying any user appearing in w continuous anonymized KGs with a confidence higher than 1/ <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>k</i> . Moreover, we introduce the Cluster-based Time-Varying Knowledge Graph Anonymization Algorithm to generate anonymized KGs satisfying k <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>w</sup> -tad. Finally, we prove that even if data providers insert/re-insert/update/delete their users, the users are protected by k <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>w</sup> -tad." @default.
- W3176806483 created "2021-07-05" @default.
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- W3176806483 date "2021-04-01" @default.
- W3176806483 modified "2023-10-16" @default.
- W3176806483 title "Privacy-Preserving Sequential Publishing of Knowledge Graphs" @default.
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- W3176806483 doi "https://doi.org/10.1109/icde51399.2021.00194" @default.
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