Big Data Provenance Using Blockchain for Qualitative Analytics via Machine Learning

【Author】 Khan, Kashif Mehboob; Haider, Warda; Khan, Najeed Ahmed; Saleem, Darakhshan

【Source】JOURNAL OF UNIVERSAL COMPUTER SCIENCE

【影响因子】1.056

【Abstract】The amount of data is increasing rapidly as more and more devices are being linked to the Internet. Big data has a variety of uses and benefits, but it also has numerous challenges associated with it that are required to be resolved to raise the caliber of available services, including data integrity and security, analytics, acumen, and organization of Big data. While actively seeking the best way to manage, systemize, integrate, and affix Big data, we concluded that blockchain methodology contributes significantly. Its presented approaches for decentralized data management, digital property reconciliation, and internet of things data interchange have a massive impact on how Big data will advance. Unauthorized access to the data is very challenging due to the ciphered and decentralized data preservation in the blockchain network. This paper proposes insights related to specific Big data applications that can be analyzed by machine learning algorithms, driven by data provenance, and coupled with blockchain technology to increase data trustworthiness by giving interference-resistant information associated with the lineage and chronology of data records. The scenario of record tampering and big data provenance has been illustrated here using a diabetes prediction. The study carries out an empirical analysis on hundreds of patient records to perform the evaluation and to observe the impact of tampered records on big data analysis i.e diabetes model prediction. Through our experimentation, we may infer that under our blockchain-based system the unchangeable and tamper-proof metadata connected to the source and evolution of records produced verifiability to acquired data and thus high accuracy to our diabetes prediction model.

【Keywords】Blockchain; Data provenance; Big data; Machine learning

【发表时间】2023

【收录时间】2023-07-31

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-机器学习