Privacy Against Agnostic Inference Attacks in Vertical Federated Learning

This academic paper examines privacy risks in vertical federated learning (a machine learning approach where different organizations each hold different features of the same data and train a model together) when facing agnostic inference attacks (attacks where the attacker doesn't know the model's structure in advance). The paper analyzes how attackers could potentially infer private information from the shared computations in this system.