Paper 2025/1310

A Comprehensive Survey of Privacy-Preserving Decision Trees Based on Homomorphic Encryption

Abstract

Decision trees are extensively employed in artificial intelligence and machine learning due to their interpretability, efficiency, and robustness-qualities that are particularly valued in sensitive domains such as healthcare, finance, and cybersecurity. In response to evolving data privacy regulations, there is an increasing demand for models that ensure data confidentiality during both training and inference. Homomorphic encryption emerges as a promising solution by enabling computations directly on encrypted data without exposing plaintext inputs. This survey provides a comprehensive review of privacy-preserving decision tree protocols leveraging homomorphic encryption. After introducing fundamental concepts and the adopted methodology, a dual-layer taxonomy is presented, encompassing system and data characteristics as well as employed processing techniques. This taxonomy facilitates the classification and comparison of existing protocols, evaluating their effectiveness in addressing key challenges related to privacy, efficiency, usability, and deploy- ment. Finally, current limitations, emerging trends, and future research directions are discussed to enhance the security and practicality of homomorphic encryption frameworks for decision trees in privacy-sensitive applications.