  Vertical federated learning (VFL) enables a service provider (i.e., active
party) who owns labeled features to collaborate with passive parties who
possess auxiliary features to improve model performance. Existing VFL
approaches, however, have two major vulnerabilities when passive parties
unexpectedly quit in the deployment phase of VFL - severe performance
degradation and intellectual property (IP) leakage of the active party's
labels. In this paper, we propose \textbf{Party-wise Dropout} to improve the
VFL model's robustness against the unexpected exit of passive parties and a
defense method called \textbf{DIMIP} to protect the active party's IP in the
deployment phase. We evaluate our proposed methods on multiple datasets against
different inference attacks. The results show that Party-wise Dropout
effectively maintains model performance after the passive party quits, and
DIMIP successfully disguises label information from the passive party's feature
extractor, thereby mitigating IP leakage.
