{"title":"基于链上和链下系统增量聚合子扇区承诺的时空大数据协同存储机制","authors":"Mingjia Han, Xinyi Yang, Huachang Su, Yekang Zhao, Ding Huang, Yongjun Ren","doi":"10.3390/ijgi13080280","DOIUrl":null,"url":null,"abstract":"As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"25 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatio-Temporal Big Data Collaborative Storage Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems\",\"authors\":\"Mingjia Han, Xinyi Yang, Huachang Su, Yekang Zhao, Ding Huang, Yongjun Ren\",\"doi\":\"10.3390/ijgi13080280\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.\",\"PeriodicalId\":48738,\"journal\":{\"name\":\"ISPRS International Journal of Geo-Information\",\"volume\":\"25 1\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-08-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISPRS International Journal of Geo-Information\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3390/ijgi13080280\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS International Journal of Geo-Information","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/ijgi13080280","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Spatio-Temporal Big Data Collaborative Storage Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems
As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.
期刊介绍:
ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.