Privacy Against Agnostic Inference Attacks in Vertical Federated Learning

This academic paper examines privacy risks in vertical federated learning (a technique where multiple organizations train AI models together while keeping their own data separate), specifically focusing on agnostic inference attacks that can expose sensitive information. The researchers analyze how attackers might infer private data even when the system doesn't require them to know the data's exact structure or type beforehand.