  SecureBoost is a tree-boosting algorithm that leverages homomorphic
encryption (HE) to protect data privacy in vertical federated learning.
SecureBoost and its variants have been widely adopted in fields such as finance
and healthcare. However, the hyperparameters of SecureBoost are typically
configured heuristically for optimizing model performance (i.e., utility)
solely, assuming that privacy is secured. Our study found that SecureBoost and
some of its variants are still vulnerable to label leakage. This vulnerability
may lead the current heuristic hyperparameter configuration of SecureBoost to a
suboptimal trade-off between utility, privacy, and efficiency, which are
pivotal elements toward a trustworthy federated learning system. To address
this issue, we propose the Constrained Multi-Objective SecureBoost (CMOSB)
algorithm, which aims to approximate Pareto optimal solutions that each
solution is a set of hyperparameters achieving an optimal trade-off between
utility loss, training cost, and privacy leakage. We design measurements of the
three objectives, including a novel label inference attack named instance
clustering attack (ICA) to measure the privacy leakage of SecureBoost.
Additionally, we provide two countermeasures against ICA. The experimental
results demonstrate that the CMOSB yields superior hyperparameters over those
optimized by grid search and Bayesian optimization regarding the trade-off
between utility loss, training cost, and privacy leakage.
