Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91728
Title: High-performance packing and searching for blockchain-based big data sharing
Authors: Jiang, Shan
Degree: Ph.D.
Issue Date: 2021
Abstract: Big data has been showing its great value in revolutionizing various industries. Generally, the big data possessed by different stakeholders forms isolated data islands, which limits their values because data cooperation can make the total value greater than the mere sum of the parts. Big data sharing is the key for the transition from data islands to data ecosystems and maximizing the value of big data. Recently, blockchain technology has been attracting intensive attention from both the industries and academic. Because of the prominent features of transparency, decentralization, and immutability, blockchain is considered as a promising solution for big data sharing. In this thesis, we study blockchain-big data sharing in terms of the basic concepts, challenging issues, and high-performance solutions. In particular, we conduct a comprehensive survey of big data sharing and present the system architecture and layered research framework of blockchain-based big data sharing. Inside the research framework, we tackle several challenges, ranging from transaction fairness, privacy preservation during data search, and anonymity of the data users, in blockchain-based big data sharing by proposing high-performance solutions. The algorithms and mechanisms are evaluated in various applications such as IoT data management and e-voting. The experimental results have indicated the high performance of our solutions. We believe this thesis can serve as a solid step towards real-world applications of blockchain-based big data sharing.
Subjects: Blockchains (Databases)
Big data
Hong Kong Polytechnic University -- Dissertations
Pages: xii, 173 pages : color illustrations
Appears in Collections:Thesis

Show full item record

Page views

21
Citations as of May 22, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.