LCPDTE: Low-Complexity Private Decision Tree Evaluation over Homomorphic Encryption (opens in new tab)

As machine-learning-as-a-service (MLaaS) becomes ubiquitous, protecting model queries via private inference is increasingly critical. Existing homomorphic encryption (HE)-based protocols for Private Decision Tree Evaluation (PDTE) have server complexity that scales at least as $O(2^D)$ in the tree depth $D$, so the cost of evaluating each tree grows exponentially with depth; in gradient boosted decision tree (GBDT) ensembles, where predictions aggregate the outputs of many trees, this per-tre...