  Vertical federated learning (VFL) has recently emerged as an appealing
distributed paradigm empowering multi-party collaboration for training
high-quality models over vertically partitioned datasets. Gradient boosting has
been popularly adopted in VFL, which builds an ensemble of weak learners
(typically decision trees) to achieve promising prediction performance.
Recently there have been growing interests in using decision table as an
intriguing alternative weak learner in gradient boosting, due to its simpler
structure, good interpretability, and promising performance. In the literature,
there have been works on privacy-preserving VFL for gradient boosted decision
trees, but no prior work has been devoted to the emerging case of decision
tables. Training and inference on decision tables are different from that the
case of generic decision trees, not to mention gradient boosting with decision
tables in VFL. In light of this, we design, implement, and evaluate Privet, the
first system framework enabling privacy-preserving VFL service for gradient
boosted decision tables. Privet delicately builds on lightweight cryptography
and allows an arbitrary number of participants holding vertically partitioned
datasets to securely train gradient boosted decision tables. Extensive
experiments over several real-world datasets and synthetic datasets demonstrate
that Privet achieves promising performance, with utility comparable to
plaintext centralized learning.
