← 研踪 PaperTrailarXiv:2611.10038 · v1
Towards a Privacy-Preserving Reliability-Aware Entity Resolution for Attributed Graphs [Vision] (Technical Report)
首次提交 2026-11-06 01:24
版本更新 2026-11-06 01:24
雷达观测 2026-11-10 10:12
摘要
Entity resolution (ER) on attributed graphs aims to identify nodes across multiple graphs that correspond to the same real-world entity and merge them into an integrated graph. When input graphs vary in quality and contain sensitive information, ER faces a dual challenge: while reliability information is crucial for accurate graph data integration, its presence in the published integrated graph can heighten privacy risks. This paper presents a research vision that unifies reliability assessment and privacy preservation within a single graph ER process. We highlight the inherent tension between these two objectives, propose a three-level coupled framework, and outline the remaining research challenges to enable the publication of integrated graphs that are both accurate and privacy-preserving.
作者备注:7 pages and 2 figures
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v1 · 2026-11-06 01:24 · 最新观测版本
Towards a Privacy-Preserving Reliability-Aware Entity Resolution for Attributed Graphs [Vision] (Technical Report)
Entity resolution (ER) on attributed graphs aims to identify nodes across multiple graphs that correspond to the same real-world entity and merge them into an integrated graph. When input graphs vary in quality and contain sensitive information, ER faces a dual challenge: while reliability information is crucial for accurate graph data integration, its presence in the published integrated graph can heighten privacy risks. This paper presents a research vision that unifies reliability assessment and privacy preservation within a single graph ER process. We highlight the inherent tension between these two objectives, propose a three-level coupled framework, and outline the remaining research challenges to enable the publication of integrated graphs that are both accurate and privacy-preserving.
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