Paper 2025/1447 Original: eprint.iacr.org/2025/1447 ↗

A New Paradigm for Privacy-Preserving Decision Tree Evaluation

Abstract

Privacy-preserving decision tree inference is a fundamental primitive in privacy-critical applications such as healthcare and finance, yet existing protocols rely heavily on secure selection, which accounts for more than half of the total cost. We introduce a new paradigm that eliminates this limitation by replacing multiple secure selections with a single permutation, whose cost is comparable to that of a single secure selection. Our scheme significantly reduces both computation and communication overhead compared to SOTA. Comprehensive benchmarks show an 86 % reduction in model evaluation versus the state-of-the-art FSS protocol by Ji et al., and a 99.9 % reduction versus the OT-based protocol of Ma et al. Overall, our benchmark shows that our protocol achieves a performance improvement of 20 tiems over Ma et al.’s scheme and 4.5 times over Ji et al.’s scheme.