Driven by the marketization of data elements and the demand for circulation, the importance of trusted data space as a new type of infrastructure has been highlighted. As a new approach in data circulation and utilization, the trusted data space enables cross-organization data sharing through three key components: process mechanisms that coordinate multiple participants, technical solutions including data usage control, privacy-preserving computation, and block chain, and consensus rules covering data, technology, business, and behavior. Together, these components aim to achieve trustworthy control, resource interaction, and value cocreation. Standing at the initial stage of the development of domestic trusted data space construction, this paper discusses the practical progress, technical realization and construction path of trusted data space, to provide feasible references for industrial landing.

In recent years, privacy-preserving computing technology has developed rapidly, its usability has continuously improved, and product types have gradually become more diverse. However, in practical applications, product security, algorithm usability, and ease of use are key factors in promoting the large-scale application of privacy-preserving computing. This paper first analyzes the needs of users for security, performance, and ease of use when using privacy-preserving computing technology, then deeply analyzes the privacy-preserving computing system based on hardware-software integration from the aspects of architecture, technology, and functions, and discusses the future development of privacy-preserving computing hardware-software integration solutions.

This paper presents a novel collaborative online dense mapping system for multiple Unmanned Aerial Vehicles (UAVs). The system confers two primary benefits: it facilitates simultaneous UAVs co-localization and real-time dense map reconstruction, and it recovers the metric scale even in GNSS-denied conditions. To achieve these advantages, Ultrawideband (UWB) measurements, monocular Visual Odometry (VO), and co-visibility observations are jointly employed to recover both relative positions and global UAV poses, thereby ensuring optimality at both local and global scales. In the proposed methodology, a two-stage optimization strategy is proposed to reduce optimization burden. Initially, relative Sim3 transformations among UAVs are swiftly estimated, with UWB measurements facilitating metric scale recovery in the absence of GNSS. Subsequently, a global pose optimization is performed to effectively mitigate cumulative drift. By integrating UWB, VO, and co-visibility data within this framework, both local geometric consistency and global pose accuracy are robustly maintained. Through comprehensive simulation and empirical real-world testing, we demonstrate that our system not only improves UAV positioning accuracy in challenging scenarios but also facilitates the high-quality, online integration of dense point clouds in large-scale areas. This research offers valuable contributions and practical techniques for precise, real-time map reconstruction using an autonomous UAV fleet, particularly in GNSS-denied environments.