Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Auxiliary Graphs
Taipan is a novel attack framework for inferring multiple sensitive attributes from public graphs without querying victim models. It achieves this by pre-training an attack model on an auxiliary graph and then adapting it to a target graph using a multi-task transfer paradigm. This exposes vulnerabilities in data sharing by showing how structural patterns leak multiple private attributes simultaneously.