SXGB: Secure and Efficient Vertical Federated XGBoost via Trusted Execution Environments
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it