A Survey of Privacy Threats and Defense in Vertical Federated Learning:
  From Model Life Cycle Perspective
Lei Yu Meng Han Yiming Li Changting Lin Yao Zhang Mingyang Zhang Yan Liu Haiqin Weng Yuseok Jeon Ka-Ho Chow Stacy Patterson
Vertical Federated Learning (VFL) is a federated learning paradigm where
multiple participants, who share the same set of samples but hold different
features, jointly train machine learning models. Although VFL enables
collaborative machine learning without sharing raw data, it is still
susceptible to various privacy threats. In this paper, we conduct the first
comprehensive survey of the state-of-the-art in privacy attacks and defenses in
VFL. We provide taxonomies for both attacks and defenses, based on their
characterizations, and discuss open challenges and future research directions.
Specifically, our discussion is structured around the model's life cycle, by
delving into the privacy threats encountered during different stages of machine
learning and their corresponding countermeasures. This survey not only serves
as a resource for the research community but also offers clear guidance and
actionable insights for practitioners to safeguard data privacy throughout the
model's life cycle.