Citation

Olaosebikan, Tahir Yinka and Haw, Su Cheng and Chan, Gaik Yee (2019) Enhancing Privacy for Big Data in Healthcare Domain based on Cryptographic and Decentralized Technology Methods. International Journal of Recent Technology and Engineering, 8 (3S). pp. 129-135. ISSN 2277-3878

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Abstract

Privacy is one of the biggest concerns that hinder most organizations to adopt the Big Data technology. Some mechanisms and systems have been set-up to handle huge databases. Nevertheless, the scalability requirements of Big Data are far beyond the conventional databases to handle. Therefore, it is trivial to set-up scalable privacy algorithms for conventional databases. Most data are stored in a single location, which means the records it keeps are open and effortlessly irrefutable to third parties. Centralized versions of this data make it too easy for hackers to attack. As such, in this paper, we present the opportunities and challenges of implementing cryptography and blockchain for privacy perseverance in Big Data, focusing in the healthcare domain. In addition, we also present some use cases of integrating Directed Acyclic Graph (DAG) into healthcare database framework for anchoring information security and privacy.

Item Type:	Article
Uncontrolled Keywords:	Big Data, Cryptography, Symmetric Encryption, Directed Acyclic Graph, Blockchain, IOTA.
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	06 Sep 2021 14:42
Last Modified:	06 Sep 2021 14:42
URII:	http://shdl.mmu.edu.my/id/eprint/8747

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