Abstract:Knowledge Graphs (KGs) are a powerful representation of linked data, offering flexibility, semantic richness, and support for knowledge enrichment and reasoning. They help data owners organize and exploit heterogeneous data to provide insightful services (e.g., recommendations), yet real-world KGs are often incomplete, hiding true facts or missing valuable insights. Knowledge graph embedding techniques are commonly used to infer valuable missing information. However, reasoning over KGs can inadvertently expose sensitive user information, even when such data is not explicitly stored. In this work, we investigate the privacy risks associated with KGE-based reasoning, focusing on attribute inference attacks where adversaries attempt to deduce sensitive user attributes from seemingly non-sensitive outputs. We propose and evaluate a framework that mitigates these privacy risks by applying post processing sanitization techniques to KGE outputs. Preliminary results demonstrate the effectiveness of these attacks on the outputs of KGE models, and explore the trade-off between recommendation quality and privacy protection when applying randomization based approaches, highlighting the need to experiment with more advanced techniques in future work to address this issue.
| Subjects: | Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.19644 [cs.CR] |
| (or arXiv:2605.19644v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.19644 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | ESWC - Extended Semantic Web Conference, May 2026, Dubrovnik, France |
From: Yasmine Hayder [view email] [via CCSD proxy]
[v1]
Tue, 19 May 2026 10:28:46 UTC (54 KB)
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