Blockchain technology has emerged as a pivotal point to enhance privacy and security in enterprise applications and cyber world. However, scalability is an issue researcher are grappling with, in large enterprises, especially in organizations bearing multiple levels of hierarchy and access privilege. Therefore, the existing models and consensus algorithms suffer one way or another. The medical or healthcare sector suffers this problem the most due to the huge amount of data and probably the central point of failure of the traditional database management system. This paper addresses the situation through a hierarchical model in Hyperledger fabric enterprise application through a healthcare sector use case. Multiple organizations are added to each hierarchy considering them as different organization levels (Hospitals, Hospital Governance, and Insurance company). Currently the first implementation has two levels of hierarchy to show networks of hospitals joining an Insurance Company. Our primary experiment revolves around this model to test and enhance the performance of the network. Performance of the model is assessed by varying and scaling environmental parameters such as the number of organizations, transaction numbers, channels, block intervals and block sizes. The benchmarking tool used is Hyperledger caliper to test various indicators such as success and failure rates along with throughput and latency. The current work only tests the scalability of the model with patient data.
Zhang S, Lee JH. Analysis of the main consensus protocols of blockchain. ICT Express. 2020;6(2):93–7.
2.
Zhang R, Preneel B. Publish or perish: a backward-compatible defense against selfish mining in bitcoin. :277–92.
3.
Pahlajani S, Kshirsagar A, Pachghare V. Survey on Private Blockchain Consensus Algorithms. 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT). IEEE; 2019. p. 1–6.
4.
Bonneau J, Narayanan A, Miller A, Clark J, Kroll JA, Felten EW. Mixcoin: Anonymity for Bitcoin with Accountable Mixes. Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2014. p. 486–504.
5.
Zheng Z, Xie S, Dai H, Chen X, Wang H. An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends. 2017 IEEE International Congress on Big Data (BigData Congress). IEEE; 2017. p. 557–64.
6.
Chauhan A, Malviya OP, Verma M, Mor TS. Blockchain and Scalability. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE; 2018.
7.
Kuo T, Ohno-Machado L. Modelchain: decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks. 2018;
8.
Zhang P, White J, Schmidt DC, Lenz G, Rosenbloom ST. FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data. Computational and Structural Biotechnology Journal. 2018;16:267–78.
9.
Ylonen T. The Secure Shell (SSH) Protocol Architecture. RFC Editor; 2006.
10.
Glicksberg BS, Burns S, Currie R, Griffin A, Wang ZJ, Haussler D, et al. Blockchain-Authenticated Sharing of Genomic and Clinical Outcomes Data of Patients With Cancer: A Prospective Cohort Study. Journal of Medical Internet Research. 2020;22(3):e16810.
11.
Lee HA, Kung HH, Udayasankaran JG, Kijsanayotin B, B Marcelo A, Chao LR, et al. An Architecture and Management Platform for Blockchain-Based Personal Health Record Exchange: Development and Usability Study. Journal of Medical Internet Research. 2020;22(6):e16748.
12.
Bosworth HB, Zullig LL, Mendys P, Ho M, Trygstad T, Granger C, et al. Health Information Technology: Meaningful Use and Next Steps to Improving Electronic Facilitation of Medication Adherence. JMIR Medical Informatics. 2016;4(1):e9.
13.
Kakei S, Shiraishi Y, Mohri M, Nakamura T, Hashimoto M, Saito S. Cross-Certification Towards Distributed Authentication Infrastructure: A Case of Hyperledger Fabric. IEEE Access. 2020;8:135742–57.
14.
Sahoo S, Fajge AM, Halder R, Cortesi A. A Hierarchical and Abstraction-Based Blockchain Model. Applied Sciences. 2019;9(11):2343.
15.
Cousot P. Abstract interpretation. ACM Computing Surveys. 1996;28(2):324–8.
16.
Hassani H, Huang X, Silva E. Big-Crypto: Big Data, Blockchain and Cryptocurrency. Big Data and Cognitive Computing. 2018;2(4):34.
17.
Jana A, Halder R, Abhishekh KV, Ganni SD, Cortesi A. Extending Abstract Interpretation to Dependency Analysis of Database Applications. IEEE Transactions on Software Engineering. 2020;46(5):463–94.
18.
Blanchet B. Security protocol verification: symbolic and computational models. 2012;3–29.
19.
Sadath L, Mehrotra D, Kumar A. Scalability in Blockchain - Hyperledger Fabric and Hierarchical Model. 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT). IEEE; 2022. p. 1–7.
20.
Jiang L, Chang X, Liu Y, Mišić J, Mišić VB. Performance analysis of Hyperledger Fabric platform: A hierarchical model approach. Peer-to-Peer Networking and Applications. 2020;13(3):1014–25.
21.
Al-Sumaidaee G, Alkhudary R, Zilic Z, Swidan A. Performance analysis of a private blockchain network built on Hyperledger Fabric for healthcare. Information Processing & Management. 2023;60(2):103160.
22.
Xu X, Sun G, Luo L, Cao H, Yu H, Vasilakos AV. Latency performance modeling and analysis for hyperledger fabric blockchain network. Information Processing & Management. 2021;58(1):102436.
23.
Kuzlu M, Pipattanasomporn M, Gurses L, Rahman S. Performance Analysis of a Hyperledger Fabric Blockchain Framework: Throughput, Latency and Scalability. 2019 IEEE International Conference on Blockchain (Blockchain). IEEE; 2019.
24.
Thakkar P, Nathan S, Viswanathan B. Performance benchmarking and optimizing Hyperledger Fabric blockchain platform. 2018;44088292.
25.
Sirur S, Nurse J, Webb H. Are we there yet? Understanding the challenges faced in complying with the General Data Protection Blockchain using hierarchical model in healthcare Regulation (GDPR). 2018;88–95.
26.
Zkik K, Belhadi A, Rehman Khan SA, Kamble SS, Oudani M, Touriki FE. Exploration of barriers and enablers of blockchain adoption for sustainable performance: implications for e-enabled agriculture supply chains. International Journal of Logistics Research and Applications. 2022;26(11):1498–535.
27.
Tan CL, Tei Z, Yeo SF, Lai KH, Kumar A, Chung L. Nexus among blockchain visibility, supply chain integration and supply chain performance in the digital transformation era. Industrial Management & Data Systems. 2022;123(1):229–52.
28.
Bag S, Rahman MS, Gupta S, Wood LC. Understanding and predicting the determinants of blockchain technology adoption and SMEs’ performance. The International Journal of Logistics Management. 2022;34(6):1781–807.
29.
Kamble SS, Gunasekaran A, Subramanian N, Ghadge A, Belhadi A, Venkatesh M. Blockchain technology’s impact on supply chain integration and sustainable supply chain performance: evidence from the automotive industry. Annals of Operations Research. 2021;327(1):575–600.
30.
Jum’a L. The role of blockchain-enabled supply chain applications in improving supply chain performance: the case of Jordanian manufacturing sector. Management Research Review. 2023;46(10):1315–33.
31.
Vishwakarma A, Dangayach GS, Meena ML, Gupta S, Luthra S. Adoption of blockchain technology enabled healthcare sustainable supply chain to improve healthcare supply chain performance. Management of Environmental Quality: An International Journal. 2022;34(4):1111–28.
32.
Li G, Xue J, Li N, Ivanov D. Blockchain-supported business model design, supply chain resilience, and firm performance. Transportation Research Part E: Logistics and Transportation Review. 2022;163:102773.
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