Research on Secure Storage Technology of Spatiotemporal Big Data Based on Blockchain

• Published in: Applied sciences 2023-07, Vol.13 (13), p.7911
• Main Authors: Zhou, Bao, Zhao, Junsan, Chen, Guoping, Yin, Ying
• Format: Article
• Language: English
• Subjects: Analysis; Batch processing; Big Data; Blockchain; Cloud computing; Collaboration; Computer storage device industry; COVID-19; Cryptography; Data analysis; Data integrity; Data processing; Data security; Digital signatures; Digital twins; Disease transmission; Hadoop Distributed File System; Information storage and retrieval; Innovations; Internet of Things; Local area networks; Metadata; New technology; spatiotemporal big data; transactions per second
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13137911

With the popularity of spatiotemporal big data applications, more and more sensitive data are generated by users, and the sharing and secure storage of spatiotemporal big data are faced with many challenges. In response to these challenges, the present paper puts forward a new technology called CSSoB (Classified Secure Storage Technology over Blockchain) that leverages blockchain technology to enable classified secure storage of spatiotemporal big data. This paper introduces a twofold approach to tackle challenges associated with spatiotemporal big data. First, the paper proposes a strategy to fragment and distribute space–time big data while enabling both encryption and nonencryption operations based on different data types. The sharing of sensitive data is enabled via smart contract technology. Second, CSSoB’s single-node storage performance was assessed under local and local area network (LAN) conditions, and results indicate that the read performance of CSSoB surpasses its write performance. In addition, read and write performance were observed to increase significantly as the file size increased. Finally, the transactions per second (TPS) of CSSoB and the Hadoop Distributed File System (HDFS) were compared under varying thread numbers. In particular, when the thread number was set to 100, CSSoB demonstrated a TPS improvement of 7.8% in comparison with HDFS. Given the remarkable performance of CSSoB, its adoption can not only enhance storage performance, but also improve storage security to a great extent. Moreover, the fragmentation processing technology employed in this study enables secure storage and rapid data querying while greatly improving spatiotemporal data processing capabilities.