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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