On the Attribute Hiding Security of Privacy Preserving Secret-Sharing-Based Outsourced Decision Tree Classification

Abstract

Decision tree classification serves as a fundamental component in many machine learning applications. As inference services are increasingly outsourced to cloud platforms, designing privacy-preserving mechanisms has become important. To secure outsourced decision tree inference, Chen et al. proposed SecDT, an efficient secret-sharing-based framework. While SecDT offers notable efficiency and preliminary security guarantees, we identify a critical vulnerability: attribute leakage. Specifically, the attributes associated with decision tree nodes are exposed to non-owner parties, which risks revealing sensitive model information. In this paper, we first present two secure enhancements, SecDT+v1 and SecDT+v2. These variants use the transformation matrix technique to obfuscate node attributes within the secret-shared domain. To prevent more advanced leakage through attribute access patterns, we design two advanced variants, SecDT+vH and SecDT+vDP, which incorporate dot-product operations to achieve the attribute-hiding property. Extensive evaluations on real-world datasets demonstrate that our proposed schemes provide robust security guarantees while outperforming state-of-the-art solutions in both classification latency and bandwidth efficiency.