Silent Guardians: Independent and Secure Decision Tree Evaluation Without Chatter

Computer Science > Cryptography and Security [Submitted on 30 Mar 2026] Silent Guardians: Independent and Secure Decision Tree Evaluation Without Chatter Jinyuan Li, Liang Feng Zhang As machine learning as a service (MLaaS) gains increasing popularity, it raises two critical challenges: privacy and verifiability. For privacy, clients are reluctant to disclose sensitive private information to access MLaaS, while model providers must safeguard their proprietary models. For verifiability, clients lack reliable mechanisms to ensure that cloud servers execute model inference correctly. Decision trees are widely adopted in MLaaS due to their popularity, interpretability, and broad applicability in domains like medicine and finance. In this context, outsourcing decision tree evaluation (ODTE) enables both clients and model providers to offload their sensitive data and decision tree models to the cloud securely. However, existing ODTE schemes often fail to address both privacy and verifiability simultaneously. To bridge this gap, we propose \sf PVODTE, a novel two-server private and verifiable ODTE protocol that leverages homomorphic secret sharing and a MAC-based verification mechanism. \sf PVODTE eliminates the need for server-to-server communication, enabling independent computation by each cloud server. This ``non-interactive'' setting addresses the latency and synchronization bottlenecks of prior arts, making it uniquely suitable for wide-area network (WAN) deployments. To our knowledge, \sf PVODTE is the first two-server ODTE protocol that eliminates server-to-server communication. Furthermore, \sf PVODTE achieves security against \emph{malicious} servers, where servers cannot learn anything about the client's input or the providers' decision tree models, and servers cannot alter the inference result without being detected. Comments: accepted by IEEE TDSC Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.28143 [cs.CR]   (or arXiv:2603.28143v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.28143 Focus to learn more Related DOI: https://doi.org/10.1109/TDSC.2026.3675596 Focus to learn more Submission history From: Jinyuan Li [view email] [v1] Mon, 30 Mar 2026 08:07:10 UTC (332 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)