Paper 2023/527

Squirrel: A Scalable Secure Two-Party Computation Framework for Training Gradient Boosting Decision Tree

Abstract

Gradient Boosting Decision Tree (GBDT) and its variants are widely used in industry, due to their strong interpretability. Secure multi-party computation allows multiple data owners to compute a function jointly while keeping their input private. In this work, we present Squirrel, a two-party GBDT training framework on a vertically split dataset, where two data owners each hold different features of the same data samples. Squirrel is private against semi-honest adversaries, and no sensitive intermediate information is revealed during the training process. Squirrel is also scalable to datasets with millions of samples even under a Wide Area Network (WAN). Squirrel achieves its high performance via several novel co-designs of the GBDT algorithms and advanced cryptography. Especially, 1) we propose a new and efficient mechanism to hide the sample distribution on each node using oblivious transfer. 2) We propose a highly optimized method for gradient aggregation using lattice-based homomorphic encryption (HE). Our empirical results show that our method can be three orders of magnitude faster than the existing HE approaches. 3) We propose a novel protocol to evaluate the sigmoid func- tion on secretly shared values, showing 19×-200×-fold im- provements over two existing methods. Combining all these improvements, Squirrel costs less than 6 seconds per tree on a dataset with 50 thousands samples which outperforms Pivot (VLDB 2020) by more than 28×. We also show that Squirrel can scale up to datasets with more than one million samples, e.g., about 170 seconds per tree over a WAN.