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We present HDP-VFL, the first hybrid differentially private (DP) frameworkfor vertical federated learning (VFL) to demonstrate that it is possible tojointly learn a generalized linear model (GLM) from vertically partitioned datawith only a negligible cost, w.r.t. training time, accuracy, etc., comparing toidealized non-private VFL. Our work builds on the recent advances in VFL-basedcollaborative training among different organizations which rely on protocolslike Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC) tosecure computation and training. In particular, we analyze how VFL'sintermediate result (IR) can leak private information of the training dataduring communication and design a DP-based privacy-preserving algorithm toensure the data confidentiality of VFL participants. We mathematically provethat our algorithm not only provides utility guarantees for VFL, but alsooffers multi-level privacy, i.e. DP w.r.t. IR and joint differential privacy(JDP) w.r.t. model weights. Experimental results demonstrate that our work,under adequate privacy budgets, is quantitatively and qualitatively similar toGLMs, learned in idealized non-private VFL setting, rather than the increasedcost in memory and processing time in most prior works based on HE or MPC. Ourcodes will be released if this paper is accepted.

Hybrid Differentially Private Federated Learning on Vertically Partitioned Data