  SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to
protect data privacy in vertical federated learning setting. It is widely used
in fields such as finance and healthcare due to its interpretability,
effectiveness, and privacy-preserving capability. However, SecureBoost suffers
from high computational complexity and risk of label leakage. To harness the
full potential of SecureBoost, hyperparameters of SecureBoost should be
carefully chosen to strike an optimal balance between utility, efficiency, and
privacy. Existing methods either set hyperparameters empirically or
heuristically, which are far from optimal. To fill this gap, we propose a
Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto
optimal solutions that each solution is a set of hyperparameters achieving
optimal tradeoff between utility loss, training cost, and privacy leakage. We
design measurements of the three objectives. In particular, the privacy leakage
is measured using our proposed instance clustering attack. Experimental results
demonstrate that the CMOSB yields not only hyperparameters superior to the
baseline but also optimal sets of hyperparameters that can support the flexible
requirements of FL participants.
