DECENTRALIZED DATA ECOSYSTEMS: A COMPREHENSIVE ANALYSIS OF STRATEGIES FOR ENHANCED RESILIENCE AND COMPLIANCE

Authors

  • Saravana Kumar Nanjappan Sri Ramakrishna Engineering College, India. Author

Keywords:

Data Decentralization, Data Governance, Data Integration, Scalable Data Architecture

Abstract

Data decentralization has emerged as a critical paradigm in modern data management, offering enhanced scalability, fault tolerance, and compliance with localized regulations. However, implementing effective decentralization strategies presents significant challenges in maintaining data accessibility, consistency, and security across distributed systems. This article proposes a comprehensive framework for achieving robust data decentralization, synthesizing best practices and leveraging cutting-edge technologies. Through a systematic review of existing literature and analysis of industry case studies, we identify key components of successful decentralization strategies, including clear objective definition, robust governance frameworks, advanced data integration tools, and AI-driven management systems. Our findings reveal that organizations implementing this holistic approach demonstrate improved data quality, enhanced operational efficiency, and adaptability to changing business requirements. The proposed framework provides a structured methodology for enterprises to navigate the complexities of data decentralization, offering insights into overcoming common pitfalls and optimizing performance in distributed data environments. This article contributes to the growing knowledge on data management strategies and offers practical guidance for organizations embarking on data decentralization initiatives.

References

M. Samaniego and R. Deters, "Blockchain as a Service for IoT," in 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016, pp. 433-436, doi: 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.102. [Online]. Available: https://ieeexplore.ieee.org/document/7917130

Y. Lu, X. Huang, Y. Dai, S. Maharjan and Y. Zhang, "Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT," in IEEE Transactions on Industrial Informatics, vol. 16, no. 6, pp. 4177-4186, June 2020, doi: 10.1109/TII.2019.2942190. [Online]. Available: https://ieeexplore.ieee.org/document/8843900

E. Brewer, "CAP twelve years later: How the "rules" have changed," in Computer, vol. 45, no. 2, pp. 23-29, Feb. 2012, doi: 10.1109/MC.2012.37. [Online]. Available: https://ieeexplore.ieee.org/document/6133253

Q. Yang, Y. Liu, T. Chen and Y. Tong, "Federated Machine Learning: Concept and Applications," in ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1-19, Jan. 2019, doi: 10.1145/3298981. [Online]. Available: https://dl.acm.org/doi/10.1145/3298981

M. Zichichi, S. Ferretti and G. D'Angelo, "A Distributed Ledger Based Infrastructure for Smart Transportation System and Social Good," 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2020, pp. 1-6, doi: 10.1109/CCNC46108.2020.9045640.

[Online]. Available: https://ieeexplore.ieee.org/document/9045640

J. Benet and N. Greco, "Filecoin: A Decentralized Storage Network," Protocol Labs, 2018. [Online]. Available: https://filecoin.io/filecoin.pdf

P. Voigt and A. von dem Bussche, "The EU General Data Protection Regulation (GDPR): A Practical Guide," Springer International Publishing, 2017. [Online]. Available: https://link.springer.com/book/10.1007/978-3-319-57959-7

S. Nakamoto, "Bitcoin: A Peer-to-Peer Electronic Cash System," 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf

P. Zhang, M. A. Walker, J. White, D. C. Schmidt and G. Lenz, "Metrics for assessing blockchain-based healthcare decentralized apps," 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, China, 2017, pp. 1-4, doi: 10.1109/HealthCom.2017.8210842. [Online]. Available: https://ieeexplore.ieee.org/document/8210842

T. Zhu, T. Huang, G. Zhang, Y. Liu and D. Liu, "DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive," IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 5, pp. 2438-2455, 1 Sept.-Oct. 2021, doi: 10.1109/TDSC.2019.2952332. [Online]. Available: https://ieeexplore.ieee.org/document/8894364

W. Wang, D. T. Hoang, P. Hu, Z. Xiong, D. Niyato, P. Wang, Y. Wen and D. I. Kim, "A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks," in IEEE Access, vol. 7, pp. 22328-22370, 2019, doi: 10.1109/ACCESS.2019.2896108. [Online]. Available: https://ieeexplore.ieee.org/document/8629877

F. Arute et al., "Quantum supremacy using a programmable superconducting processor," Nature, vol. 574, pp. 505–510, 2019, doi: 10.1038/s41586-019-1666-5. [Online]. Available: https://www.nature.com/articles/s41586-019-1666-5

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Published

2024-09-25

How to Cite

Saravana Kumar Nanjappan. (2024). DECENTRALIZED DATA ECOSYSTEMS: A COMPREHENSIVE ANALYSIS OF STRATEGIES FOR ENHANCED RESILIENCE AND COMPLIANCE. INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY RESEARCH (IJETR), 9(2), 321-334. https://lib-index.com/index.php/IJETR/article/view/IJETR_09_02_029