  The emergence of vertical federated learning (VFL) has stimulated concerns
about the imperfection in privacy protection, as shared feature embeddings may
reveal sensitive information under privacy attacks. This paper studies the
delicate equilibrium between data privacy and task utility goals of VFL under
differential privacy (DP). To address the generality issue of prior arts, this
paper advocates a flexible and generic approach that decouples the two goals
and addresses them successively. Specifically, we initially derive a rigorous
privacy guarantee by applying norm clipping on shared feature embeddings, which
is applicable across various datasets and models. Subsequently, we demonstrate
that task utility can be optimized via adaptive adjustments on the scale and
distribution of feature embeddings in an accuracy-appreciative way, without
compromising established DP mechanisms. We concretize our observation into the
proposed VFL-AFE framework, which exhibits effectiveness against privacy
attacks and the capacity to retain favorable task utility, as substantiated by
extensive experiments.
