Secure inference enables privacy-preserving machine learning by leveraging cryptographic protocols that support computations on sensitive user data without exposing it. However, integrating cryptographic protocols with large language models (LLMs) presents significant challenges, as the inherent complexity of these protocols, together with LLMs' massive parameter scale and sophisticated architectures, severely limits practical usability. In this work, we propose ENSI, a novel non-interactive secure inference framework for LLMs, based on the principle of codesigning the cryptographic protocols and LLM architecture. ENSI employs an optimized encoding strategy that seamlessly integrates CKKS scheme with a lightweight LLM variant, BitNet, significantly reducing the computational complexity of encrypted matrix multiplications. In response to the prohibitive computational demands of softmax under homomorphic encryption (HE), we pioneer the integration of the sigmoid attention mechanism with HE as a seamless, retraining-free alternative. Furthermore, by embedding the Bootstrapping operation within the RMSNorm process, we efficiently refresh ciphertexts while markedly decreasing the frequency of costly bootstrapping invocations. Experimental evaluations demonstrate that ENSI achieves approximately an$8 \times$acceleration in matrix multiplications and a$2.6 \times$speedup in softmax inference on CPU compared to state-of-the-art method, with the proportion of bootstrapping is reduced to just 1 %.

Federated learning (FL) has emerged as a prominent methodology for collaboratively training machine learning models among multiple participants while alleviating data privacy leakage through data localization. However, recent studies have shown that the transferred intermediate parameters still contain sensitive information that needs to be further protected. More-over, real-world institutions often possess diverse data attributes, necessitating the adoption of Vertical Federated Learning (VFL) for cooperative learning tasks. Existing researches in VFL have proposed some frameworks with privacy-preservation functionality, yet they suffer from high participant overhead or low model accuracy, etc. To address these challenges, in this paper, we propose EzBoost, a fast and secure vertical federated tree boosting framework built upon XGBoost. Specifically, we leverages the efficient Secure Multi-party Computation (MPC) framework, EzPC, to facilitate the design and implementation of EzBoost. By carefully designing our framework with two non-collusive servers for secure two-party computation, EzBoost significantly accelerates the runtime of model training and querying at most 20×, and reduces the participant overheads at most 300×. In addition, we identify a potential privacy leakage problem in recent researches and propose a more robust solution for addressing it. Through comprehensive security analysis and comparative experiments with existing approaches, we demonstrate that EzBoost achieves stronger privacy-preservation, higher accuracy and higher efficiency simultaneously.