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

Abstract

As a classical type of machine learning algorithms, tree models have been widely employed in various fields, such as financial analysis and health diagnostics, offering high-accuracy and low-latency prediction services to users. However, tree evaluation also raises significant privacy concerns, particularly with respect to the tree model and the query sample, while the existing private decision tree evaluation schemes are unable to reach a good trade-off between privacy and efficiency in practice. Therefore, in this paper, we propose an efficient and privacy-preserving tree evaluation scheme based on additive homomorphic encryption, namely PACT. Specifically, PACT introduces an innovative algorithm by leveraging the overflow characteristic of two’s complement to support AHE-based parallel comparison, and it utilizes the lightweight homomorphic addition to select tree paths non-interactively. Meanwhile, we carefully design perturbation and shuffle methods to enhance model and sample privacy. The security of PACT is verified based on the ideal-real paradigm. Experimental results on real-world and synthetic datasets demonstrate the lossless accuracy and superior running efficiency of PACT.