Abstract Transport Layer Cryptography Protocol (TLCP) is a secure communication protocol developed in China, featuring a dual-certificate architecture and incorporating ShangMi cryptographic algorithms. It has been widely deployed in security-critical domains such as finance, government, and energy. Despite its practical significance, TLCP did not undergo comprehensive formal analysis during its standardization process, leaving potential design-level vulnerabilities insufficiently explored. Moreover, the advent of quantum computing poses fundamental challenges to the classical cryptographic primitives employed by TLCP, motivating the need for both systematic security evaluation and post-quantum enhancements. To address these gaps, we first construct the comprehensive formal model of TLCP, covering certificate-based and identity-based cipher suites as well as its distinctive dual-certificate mechanism, under a realistic threat model and security assumptions that capture both classical and quantum adversaries. Based on this model, we conduct an automated security analysis using ProVerif, identifying nine potential attack vectors and deriving five concrete mitigation recommendations. Finally, motivated by the analysis results and the limitations of incremental fixes against quantum threats, we propose KEMTLCP, a post-quantum secure variant of TLCP that leverages key encapsulation mechanisms (KEMs) for both key exchange and authentication while preserving TLCP’s architectural principles through a novel explicit authentication mechanism. We further provide a security proof for the core authentication mechanism, show that KEMTLCP effectively mitigates the majority of identified vulnerabilities through formal analysis, and evaluate its practical performance.

We present a secure backpropagation neural network training model (SecureBP), which allows a neural network to be trained while retaining the confidentiality of the training data, based on the homomorphic encryption scheme. We make two contributions. The first one is to introduce a method to find a more accurate and numerically stable polynomial approximation of functions in a certain interval. The second one is to find a strategy of refreshing ciphertext during training, which keeps the order of magnitude of noise at O˜e33 .