SXGB: Secure and Efficient Vertical Federated XGBoost via Trusted Execution Environments

Driven by increasingly tighter privacy restrictions, federated learning (FL), involving training global machine learning models over multiple participants while keeping their data localized, has shown its advantages and gained extensive attention. Despite the promising future, security and efficiency remain major concerns hindering its further development. In this paper, we propose a secure and efficient vertical FL scheme for the XGBoost model, named SXGB, in which the trusted execution environments (TEEs) are introduced to improve the efficiency without an additional security assumption. Specifically, we first design a bucket sharing algorithm to encode and secretly share participants' data buckets. Then, through a combination of the TEEs and symmetric homomorphic encryption techniques, we propose a secure split finding algorithm to accurately find the best splits while ensuring the privacy of data inside and outside the enclave. Moreover, a fingerprint verification method is embedded into the split finding algorithm to ensure the honest execution of the training program. A detailed security analysis shows that SXGB can effectively defend against inference and tamper attacks. Extensive experiments demonstrate that SXGB offers at least a 20× improvement in communication efficiency and a 10× speedup of running time compared to existing representative schemes.