Today cloud computing is widely used in various industries. While benefiting from the services provided by the cloud, users are also faced with some security issues, such as information leakage and data tampering. Utilizing trusted computing technology to enhance the security mechanism, defined as trusted cloud, has become a hot research topic in cloud security. Currently, virtual TPM (vTPM) is commonly used in a trusted cloud to protect the integrity of the cloud environment. However, the existing vTPM scheme lacks protections of vTPM itself at a runtime environment. This paper proposed a novel scheme, which designed a new trusted cloud platform security component, ‘enclave TPM (eTPM)’ to protect cloud and employed Intel SGX to enhance the security of eTPM. The eTPM is a software component that emulates TPM functions which build trust and security in cloud and runs in ‘enclave’, an isolation memory zone introduced by SGX. eTPM can ensure its security at runtime, and protect the integrity of Virtual Machines (VM) according to user-specific policies. Finally, a prototype for the eTPM scheme was implemented, and experiment manifested its effectiveness, security, and availability.

Rapid growth of internet data has created enormous security challenges on authenticity, availability and integrity protection of these outsourced data. Hash function is one of the main solutions to face these challenges. This paper proposes a feedback iterative structure of hash function which utilises the variable feedback to resist attacks. Furthermore, to accelerate message diffusion, two novel modules are designed, one for iteration and the other for truncation. Experimental results show that the proposed hash function can effectively resist existing attacks. Moreover, comparing with other existing hash functions, it displays better on statistical performance, collision resistance and avalanche.

Real-time detection of small obstacles is a critical challenge for autonomous systems such as self-driving vehicles, unmanned aerial vehicles (UAVs), and mobile robots. These small obstacles (e.g., road debris, fallen branches, cables) pose significant safety risks due to their low visibility and irregular appearances. This paper presents a comprehensive systematic review of 117 technical articles published between 2016 and 2025, focusing on the techniques and deployment strategies for real-time small obstacle detection using fused multidimensional information. We summarize and analyze developments in small obstacle definitions, sensing hardware, detection algorithms, fusion methods, and real-time optimization techniques. Our findings reveal a growing trend toward integrating multiscale learning, multimodal fusion, and lightweight models for deployment in resource-constrained environments. However, challenges such as performance consistency across platforms, lack of standard definitions, and insufficient deployment practices persist. This review identifies future research opportunities and provides recommendations to guide further advances in robust, scalable, and deployable small obstacle detection systems.

A map traceability management scheme for security control is proposed utilising visual cryptography and Quick Response Code. The map information is classified into two categories: the public information MPI and the traceable map information MTI, and they are both converted into QR codes. Map shares MS and control shares CS are generated according to extended multiple visual cryptographic scheme. MPI can be obtained by scanning the MS, while MTI can be got by overlaying the MS on CS which is only accessible to management units, so it can identify every map and provide the basis for the traceability management.