A Secure Big Data Storage Framework Based on Blockchain Consensus Mechanism With Flexible Finality

【Author】 Sasikumar, A.; Ravi, Logesh; Kotecha, Ketan; Abraham, Ajith; Devarajan, Malathi; Vairavasundaram, Subramaniyaswamy

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Data security and integrity are becoming increasingly important as the volume of data being created and stored grows. A controlled third party that provides most of the existing big data security systems makes them susceptible to several security risks. By resolving current technology challenges, including scalability, non-tampering, trustworthiness, data governance, and transparency, blockchain technology plays a vital role and has a significant potential to safeguard personal information. Therefore, this work focuses on addressing real-time big data storage issues based on a transdisciplinary research approach. This study introduces a brand-new approach to big data storage security that leverages blockchain technology and applies highway protocol to generate new blocks. The proposed highway protocol works based on the flexible finality condition to overcome issues of baseline models. The highway allows blocks to run the consensus mechanism to configure security thresholds more freely. The proposed protocol also allows blocks with lower thresholds to reach finality more quickly than blocks requiring greater degrees of confidence. Therefore, the proposed big data framework can dynamically control data manipulation and continuously support individuals to participate in the data-sharing process. A comparison was performed with the number of data requests in terms of hit ratio, and a highway protocol provides better results than baseline models. The proposed model provides a data processing period of 13 to 30 ms and an energy consumption of 32 to 41 mJ.

【Keywords】Big Data; Blockchains; Security; Internet of Things; Government; Data security; Storage management; Big data storage; blockchain; consensus mechanism; highway protocol; flexible finality

【发表时间】2023

【收录时间】2023-07-09

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-大数据