With the increasing demand for data security and flexible access control in In-Vehicle Networks (IVNs), traditional Automotive Ethernet and its application-layer protocols lack built-in authentication mechanisms, failing to meet current fine-grained access control requirements for IVNs. In IVN environments, the constrained computing resources of in-vehicle devices further complicate the implementation of fine-grained access control. To address the challenges of realizing fine-grained access control under resource constraints and high real-time communication demands in IVNs, a dynamic data access control strategy suitable for Advanced Driver Assistance Systems (ADAS) is investigated. Lightweight elliptic curve cryptography is introduced to reduce computational overhead and improve execution efficiency. A dynamic attribute revocation mechanism is designed to support precise revocation of a single attribute without affecting other entities. Experimental results demonstrate that the proposed strategy achieves efficient and scalable access control for resource-constrained IVNs while ensuring security. This strategy provides a practical solution for secure and flexible data access in ADAS scenarios, balancing performance and security requirements of in-vehicle communication systems.

With the proposal of the Action Plan for the Development of Trusted Data Space, promoting the transaction and circulation of data elements has risen to the level of national strategy. Cryptographic technology, serving as the technical core and fundamental pillar of network security, provides confidentiality, integrity, authenticity, and non-repudiation, thereby robustly underpinning data security within trusted data space. Focusing on data element circulation scenarios, this paper constructs a commercial cryptography security assurance system from three dimensions: technology empowerment, standards alignment, and management synergy. By analyzing security application requirements across the entire data life cycle, a layered and decoupled cryptographic application architecture is proposed. A standard system ranging from foundational general specifications to application evaluation is established, along with a comprehensive management mechanism that integrates institutional norms, supervision, evaluation, and policy coordination. Comparative analysis with traditional data security and cryptographic application systems demonstrates that the proposed system exhibits significant advantages in the depth of integration between data business and cryptographic functions, full-chain security protection, and synergy between standards and management. It provides both a theoretical reference and a practical pathway for the secure and efficient circulation of data elements.

Trusted data space is an infrastructure that ensures the secure circulation of data, with the development of the digital economy, new requirements have been proposed for the security of data storage, transmission, and sharing in trusted data spaces, and cryptographic technology can provide strong security protection for the development of trusted data spaces in terms of distribution, scalability, and cross-domain collaboration. Based on a comprehensive review of the domestic and international research status on trusted data space key management, explored from three dimensions: key management rrchitecture and mechanisms, key lifecycle management, and cross-domain authentication and negotiation. Focused on analyzing hierarchical, distributed, and lightweight key mechanisms, as well as key technologies such as key updating, storage hosting, and proxy re-encryption, and summarized methods for cross-domain authentication and group key negotiation, and further points out the deficiencies in current research concerning dynamic adaptation and cross-domain collaborative efficiency. Finally, new development directions such as adaptive key management and intelligent secure key management in the trusted data space were explored.

Generative text steganography primarily utilizes language models to generate steganographic text, however, previous methods typically trained on a single corpus, generated steganographic texts of random lengths, failing to adapt to varying message types and channel conditions. To address this, we proposed a large language model-assisted intelligent text steganographic transmission method to reduce transmission overhead. We parameter-efficiently fine-tuned a large language model on multiple corpora to obtain adapters for different corpus styles, and using the model's token probability distribution, recursively embedded secret information via adaptive dynamic grouping. We then designed a dual-agent reinforcement learning framework that adaptively optimizes the steganographic strategy—including corpus type and sentence length—and selects transmission power based on real-time channel state and security indicators such as attack success rate. This method considered both steganographic security and the transmission environment, constructed a reward function to evaluate each strategy, and guided strategy and power selection to balance concealment and robustness. The results showed that, compared to direct use of adaptive dynamic grouping, our method reduced delay by 38.5% and attack success rate to 0.83%, while maintaining similar embedding rates and information divergence. Consequently, the proposed method achieves a good balance between stealthiness and transmission robustness under varying channel conditions and message types.

Neural networks, as powerful nonlinear modeling tools, demonstrate unique advantages across multiple fields and have recently begun to show their potential in the field of cryptography, providing new technical insights for encrypted data analysis. The deep neural network model is utilized to learn the mapping relationship from the internal functions of the known encryption algorithm, and an encryption neural network model functionally equivalent to the original algorithm is constructed based on the chain of thought pattern. Through the meticulous design of efficient neural network structures and the construction of appropriate training datasets, the equivalent neural network simulation of basic operations (XOR, OR, AND, modular addition, cyclic shift, S-box transformation, row shift and column mixing) for block cipher algorithms is realized. On this basis, the learning of encryption and key expansion functions is conducted on four typical block cipher algorithms, namely AES-128, SM4, SIMON32/64 and SPECK64/96, and the accuracy and stability of the model in different application scenarios are verified. Experimental results show that the simulation success rate of the model on AES-128, SM4 and SIMON32/64 reaches 100%, and the success rate on SPECK64/96 reaches 97%. The equivalent neural network learning of block cipher algorithms based on chain of thought cannot only accurately learn the operation logic of the original encryption algorithm, but also effectively simulate its key expansion process, with high simulation accuracy and stability.

Automotive millimeter-wave radar has been widely applied in autonomous driving due to its low cost and strong robustness to adverse conditions such as illumination and smoke. However, with the increasing number of radars on roads and the growing scarcity of spectrum resources (77~81GHz), mutual interference among these radars has become a critical issue. The resulting ghost targets degrade radar sensitivity and significantly increase the false alarm rate, posing a serious threat to autonomous driving safety. To address this problem, this paper proposes a multi-domain joint ghost target removal method integrating angle of departure (AoD), angle of arrival (AoA), and Doppler information. A key finding is that interference signals have asymmetric transmission characteristics compared with target echoes: target echoes involve round-trip propagation, whereas interference involves one-way propagation. Initially, this paper performs joint angle estimation in the AoD-AoA domains. By exploiting the absence of AoD information in one-way interference signals, this paper achieves the separation of interference and target echoes in the angular domain. For ghost targets near the zero-degree angle, this paper introduces pseudo-random coding in the slow time domain to convert them into the Doppler domain, where they are effectively removed through an optimization model designed to maximize the signal-to-interference-plus-noise ratio (SINR). Experimental results demonstrate that the proposed method can effectively remove ghost targets caused by inter-radar interference, thereby enhancing the perceptual reliability of radars in complex electromagnetic environments.