PACT: Enhancing Privacy and Efficiency in Tree Evaluation via Secure Parallel Comparison and Oblivious Tree Aggregation

Summary

The paper presents PACT, a privacy-preserving tree model evaluation scheme built on additive homomorphic encryption. It supports parallel comparison by exploiting the overflow behavior of two's complement, and selects tree paths non-interactively using homomorphic addition, with perturbation and shuffle methods to protect model and query sample privacy. Experiments on real-world and synthetic datasets report lossless accuracy and better running efficiency than existing private decision tree evaluation schemes.