Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Auxiliary Graphs

תקציר מקורי באנגליתarXiv:2602.06700v2 Announce Type: replace-cross Abstract: Graph-structured data underpin a wide spectrum of modern applications, yet their multiple sensitive attributes are not isolated but deeply coupled with graph topology. This coupling facilitates intersectional privacy leakage through attribute inference attacks (AIAs). Existing AIAs predominantly assume adversaries with two capabilities: (I) access to either auxiliary data sampled from the same distribution as the target or partial training records, and (II) the ability to repeatedly query the victim model. In reality, however, these assumptions are often impractical due to stringent data protection regulations, prohibitive query budgets, and heightened detection risks. Moreover, as prior attacks are designed to infer a single sensit