Yan Kang

Yan Kang, Ziyao Ren, Lixin Fan et al.

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.

Hanlin Gu, Jiahuan Luo, Yan Kang et al.

Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific conditions, has triggered numerous follow-up research in designing powerful attacking methods and effective defending mechanisms aiming to thwart these attacking methods. Nevertheless, privacy-preserving mechanisms employed in these defending methods invariably lead to compromised model performances due to a fixed obfuscation applied to private data or gradients. In this article, we, therefore, propose a novel adaptive obfuscation mechanism, coined FedAdOb, to protect private data without yielding original model performances. Technically, FedAdOb utilizes passport-based adaptive obfuscation to ensure data privacy in both horizontal and vertical federated learning settings. The privacy-preserving capabilities of FedAdOb, specifically with regard to private features and labels, are theoretically proven through Theorems 1 and 2. Furthermore, extensive experimental evaluations conducted on various datasets and network architectures demonstrate the effectiveness of FedAdOb by manifesting its superior trade-off between privacy preservation and model performance, surpassing existing methods.