While knowledge graphs (KGs) are getting popular as they can formalize many types of users’ data in social networks, sharing these data may reveal users’ identities. Although many protection models have been presented to protect users in anonymized data, they are unsuitable to protect the users in KGs. To cope with this problem, we propose k-AttributeDegree (k-ad), a model to protect users’ identities in anonymized KGs. We further present information loss metrics tailored to KGs and a cluster-based anonymization algorithm to generate anonymized KGs satisfying k-ad. Finally, we conduct experiments on five real-life data sets to evaluate our algorithm and compare it with previous work.

Cluster-Based Anonymization of Knowledge Graphs

Carminati B.;Ferrari E.
2020-01-01

Abstract

While knowledge graphs (KGs) are getting popular as they can formalize many types of users’ data in social networks, sharing these data may reveal users’ identities. Although many protection models have been presented to protect users in anonymized data, they are unsuitable to protect the users in KGs. To cope with this problem, we propose k-AttributeDegree (k-ad), a model to protect users’ identities in anonymized KGs. We further present information loss metrics tailored to KGs and a cluster-based anonymization algorithm to generate anonymized KGs satisfying k-ad. Finally, we conduct experiments on five real-life data sets to evaluate our algorithm and compare it with previous work.
2020
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
978-3-030-57877-0
978-3-030-57878-7
18th International Conference on Applied Cryptography and Network Security, ACNS 2020
ita
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2124722
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