  Vertical Federated Learning (VFL) is widely utilized in real-world
applications to enable collaborative learning while protecting data privacy and
safety. However, previous works show that parties without labels (passive
parties) in VFL can infer the sensitive label information owned by the party
with labels (active party) or execute backdoor attacks to VFL. Meanwhile,
active party can also infer sensitive feature information from passive party.
All these pose new privacy and security challenges to VFL systems. We propose a
new general defense method which limits the mutual information between private
raw data, including both features and labels, and intermediate outputs to
achieve a better trade-off between model utility and privacy. We term this
defense Mutual Information Regularization Defense (MID). We theoretically and
experimentally testify the effectiveness of our MID method in defending
existing attacks in VFL, including label inference attacks, backdoor attacks
and feature reconstruction attacks.
