###### Abstract  
The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security, Blockchain has great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind the software-defined network (SDN) technology, this paper envisions SAUSA, a Blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud datasets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-inspired point cloud service architecture, which allows for efficient data processing and delivery to satisfy diverse quality-of-service (QoS) requirements. A Blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the Blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability, but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution. Blockchain; smart contract; point cloud; security; privacy preservation; software-defined network (SDN); big data; assurance; resilience +
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Therefore, point clouds have been widely adopted in numerous application fields, including 3D scanning and modeling, environmental monitoring, agricultural and forestry, bio-medical imagery, and so on [2]. Recently, deep learning (DL) on point clouds has been thriving in many scene-understanding-related applications, such as virtual/augmented reality (VR/AR), autonomous driving, and robotics. Nevertheless, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for securing data collection, management, storage, and sharing. By using signal processing or neural network techniques, several efficient point cloud compression (PCC) methods [3] have been proposed to reduce the bandwidth of wireless networks or the storage space of 3D point cloud raw data. However, there are still many efforts to achieve efficient end-to-end data delivery and optimal storage management. From the architecture aspect, conventional point-cloud-based applications rely on centralized cloud servers for data collection and analysis. Such a centralized manner is prone to single-point failures because any successful attacks such as distributed denial-of-service (DDoS) to the control (or data) server may paralyze the entire system. Other than that, a centralized server that manages 3D sensors and stores point clouds under a distributed network environment may lead to performance bottlenecks (PBNs), and it is vulnerable to data breaches caused by curious third parties and security threats in data acquisition, storage, and sharing process.