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New framework tackles privacy risks in knowledge graph embeddings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a framework to identify and mitigate privacy risks in knowledge graph embeddings (KGEs). The study demonstrates how adversaries can infer sensitive user attributes from KGE outputs, even when this information is not explicitly stored. The proposed defense mechanism involves post-processing KGE results to sanitize them, balancing recommendation quality with enhanced privacy protection. AI

IMPACT This research highlights potential privacy vulnerabilities in knowledge graph embeddings, prompting the development of new defense strategies to protect sensitive user data.

RANK_REASON This is a research paper detailing new methods for attacking and defending privacy in knowledge graph embeddings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework tackles privacy risks in knowledge graph embeddings

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This is a research paper detailing new methods for attacking and defending privacy in knowledge graph embeddings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yasmine Hayder ·

    Inferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies

    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), …

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