As cross-border access becomes more frequent, traditional perimeter-based network security models can no longer cope with evolving security requirements. Zero trust is a novel paradigm for cybersecurity based on the core concept of “never trust, always verify”. It attempts to protect against security risks related to internal threats by eliminating the demarcations between the internal and external network of traditional network perimeters. Nevertheless, research on the theory and application of zero trust is still in its infancy, and more extensive research is necessary to facilitate a deeper understanding of the paradigm in academia and the industry. In this paper, trust in cybersecurity is discussed, following which the origin, concepts, and principles related to zero trust are elaborated on. The characteristics, strengths, and weaknesses of the existing research are analysed in the context of zero trust achievements and their technical applications in Cloud and IoT environments. Finally, to support the development and application of zero trust in the future, the concept and its current challenges are analysed.

A new blind watermarking scheme for three-dimensional point-cloud models is proposed based on vertex curvature to achieve an appropriate trade-off between transparency and robustness. The root mean square curvature of local set of every vertex is first calculated for the three-dimensional point-cloud model and then the vertices with larger root mean square curvature are used to carry the watermarking information; the vertices with smaller root mean square curvature are exploited to establish the synchronization relation between the watermark embedding and extraction. The three-dimensional point-cloud model is divided into ball rings, and the watermarking information is inserted by modifying the radial radii of vertices within ball rings. Those vertices taking part in establishing the synchronization relation do not carry the watermarking information; therefore, the synchronization relation is not affected by the embedded watermark. Experimental results show the proposed method outperforms other well-known three-dimensional point-cloud model watermarking methods in terms of imperceptibility and robustness, especially for against geometric attack.

A 3D model watermarking method robust to geometric attacks is proposed. The vertices of the model are classified into three groups. The vertices of the low‐resolution group are used to establish an invariant space in which to resist geometric attacks. In the medium‐resolution group, appropriate vertices in which to embed the watermarking information are selected. The selection of vertices that contain information about the watermark is based on the area of the local set of the vertex, the curvature of which determines the embedding strength of the watermark. The vertices of the high‐resolution group are reserved for resisting simplification and smoothing attacks. The choice of the embedding position and the embedding strength can provide a suitable trade‐off between good transparency and maximum robustness of the proposed method. The simulation results show that, compared to existing state‐of‐the‐art methods, the proposed method is robust against attacks such as noise, smoothing, simplification, cropping, rotation, translation, and scaling while ensuring high visual quality of the watermarked model.

This paper presents a new blind watermarking scheme for 3-D point cloud models by using feature vertices to locate the embedded watermark. The vertices with larger mean curvature are considered to be feature vertices carrying watermarking information; non-feature vertices are used to build a new coordinate system in which the 3-D point cloud model is classified into bins containing several longitudes. Each bit of the watermarking information is repeatedly embedded into a bin by altering azimuth angles of feature vertices lying in the bin. The non-feature vertices are not exploited to embed the watermarking information, which enables the built coordinate system to avoid the impacts of the embedding of watermark. The choice of the embedding position and redundant embedding provide an optimal balance between imperceptibility and robustness of watermark. Simulation results testify that our watermarking method for 3-D point cloud models has desired robustness against common attack and geometric attack while demonstrating good transparency.