Privacy-preserving Decision Graph Inference from Homomorphic Lookup Table

This paper studies MPC based decision graph inference (MDGI) where decision graphs (generalization of decision trees) are very popular machine learning models. In MDGI, a modeler holding a private model and a data owner holding private feature vectors jointly run model inference on each vector through an MPC protocol, which outputs the inference result without revealing the model or vector. Several noteworthy MDGI solutions have been proposed, but they remain unsatisfactory for large models due to the high communication cost of oblivious decision, the most complex component in MDGI. Oblivious decision securely evaluates binary tests (Boolean-valued functions) over features without revealing test type, parameter, feature index, or value. All constant-round oblivious decision protocols suffer from high communication costs due to bitwise encryption and transmission. Moreover, most support only one of the three common test types: threshold comparison, equality test, and set containment. We propose Homomorphic Lookup Table (HLT), a novel MPC technique for oblivious decision. HLT circumvents the bitwiseencryption and heavy-communication issue by adopting tablelookup-style computation and amortizing the cost of encrypted table transfer across many inferences. We carefully design the table structure and lookup rules, and integrate homomorphic encryption to optimize performance and support multiple test types. HLT achieves 78 × −4151× reduction in communication for oblivious decision, and is the first method to support all three common test types. Based on HLT, we design constant-round MDGI solutions for two widely used decision graphs: decision trees and scorecards. This is the first privacy-preserving solution for scorecards, and our decision tree solution reduces communication by 27 × −321×.