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Modern cloud-based machine learning (ML) services raise significant privacy concerns, as users must share potentially sensitive data with service providers. Private Decision Tree Evaluation (PDTE) addresses this by allowing encrypted queries to be evaluated against a model without revealing either party’s data, typically using Homomorphic Encryption (HE). While existing HE-based frameworks have de
