Scalability Performance Analysis of Blockchain Using Hierarchical Model in Healthcare
DOI:
https://doi.org/10.30953/bhty.v7.295Keywords:
blockchain technology, database management, hyperledger, Hyperledger Caliper, network performanceAbstract
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.
Downloads
References
Zhang S, Lee JH. Analysis of the main consensus protocols of blockchain. ICT Express. 2020 Jun 1;6(2):93–7. https://doi.org/10.1016/j.icte.2019.08.001
Zhang R, Preneel B. Publish or perish: a backward-compatible defense against selfish mining in bitcoin. In Topics in Cryptology–CT-RSA 2017: the cryptographers’ track at the RSA Conference 2017, San Francisco, CA, USA, February 14–17, 2017, Proceedings 2017 (pp. 277–292). Springer International Publishing. Available from: https://www.esat.kuleuven.be/cosic/publications/article-2746.pdf.
Pahlajani S, Kshirsagar A, Pachghare V. Survey on private blockchain consensus algorithms. 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), Chennai, India, 2019, (pp. 1–6). https://doi.org/10.1109/ICIICT1.2019.8741353
Bonneau J, Narayanan A, Miller A, Clark J, Kroll JA, Felten EW. Mixcoin: anonymity for bitcoin with accountable mixes. In: R. Safavi-Naini & N. Christin (Eds.). Financial Cryptography and Data Security - 18th International Conference, FC 2014, Revised Selected Papers. Springer Verlag. 2014. (pp. 486–504). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-662-45472-5_31
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), Honolulu, HI, USA, 2017, (pp. 557–5640). https://doi.org/10.1109/BigDataCongress.2017.85
Chauhan A, Malviya OP, Verma M, Mor TS. Blockchain and scalability. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), Lisbon, Portugal, 2018, (pp. 122–128). https://doi.org/10.1109/QRS-C.2018.00034
Kuo TT, Ohno-Machado L. Modelchain: decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks. arXiv preprint arXiv:1802.01746. 2018. https://doi.org/10.48550/arXiv.1802.01746
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 Jan 1;16:267–78. https://doi.org/10.1016/j.csbj.2018.07.004
Ylonen T, Lonvick C. The secure shell (SSH) protocol architecture. 2006 Jan. https://doi.org/10.17487/RFC4251
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. J Med Internet Res. 2020 Mar 20;22(3):e16810. https://doi.org/10.2196/16810
Lee HA, Kung HH, Udayasankaran JG, Kijsanayotin B, Marcelo A, Chao LR, et al. An architecture and management platform for blockchain-based personal health record exchange: development and usability study. J Med Internet Res. 2020 Jun 9;22(6):e16748. https://doi.org/10.2196/16748
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 Med Inform. 2016 Mar 15;4(1):e4326. https://doi.org/10.2196/medinform.4326
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 Jul 22;8:135742–57. https://doi.org/10.1109/ACCESS.2020.3011137
Sahoo S, Fajge AM, Halder R, Cortesi A. A hierarchical and abstraction-based blockchain model. Appl Sci. 2019 Jun 7;9(11):2343. https://doi.org/10.3390/app9112343
Cousot P. Abstract interpretation. ACM Computing Surveys (CSUR). 1996 Jun 1;28(2):324–8. https://doi.org/10.1145/234528.234740
Hassani H, Huang X, Silva E. Big-crypto: big data, blockchain and cryptocurrency. Big Data Cogn Comput. 2018 Oct 19;2(4):34. https://doi.org/10.3390/bdcc2040034
Jana A, Halder R, Abhishekh KV, Ganni SD, Cortesi A. Extending abstract interpretation to dependency analysis of database applications. IEEE Transac Softw Eng. 2018 Jul 31;46(5):463–94. https://doi.org/10.1109/TSE.2018.2861707
Blanchet B. Security protocol verification: symbolic and computational models. In International conference on principles of security and trust 2012 Mar 24 (pp. 3–29). Berlin, Heidelberg: Springer Berlin Heidelberg.
Sadath L, Mehrotra D, Kumar, V. Scalability in Blockchain- Hyperledger Fabric and Hierarchical Model. 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT), New Delhi, India, 2022, (pp. 1–7). https://doi.org/10.1109/GlobConPT57482.2022.9938147
Jiang L, Chang X, Liu Y, Mišić J, Mišić VB. Performance analysis of Hyperledger Fabric platform: a hierarchical model approach. Peer-to-Peer Netw Appl. 2020 May;13:1014–25. https://doi.org/10.1007/s12083-019-00850-z
Al-Sumaidaee G, Alkhudary R, Zilic Z, Swidan A. Performance analysis of a private blockchain network built on Hyperledger Fabric for healthcare. Inform Process Manag. 2023 Mar 1;60(2):103160. https://doi.org/10.1016/j.ipm.2022.103160
Xu X, Sun G, Luo L, Cao H, Yu H, Vasilakos AV. Latency performance modeling and analysis for Hyperledger Fabric blockchain network. Inform Process Manag. 2021 Jan 1;58(1):102436. https://doi.org/10.1016/j.ipm.2020.102436
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), Atlanta, GA, USA, 2019, (pp. 536–540). https://doi.org/10.1109/Blockchain.2019.00003
Thakkar P, Nathan S, Viswanathan B. Performance benchmarking and optimizing Hyperledger Fabric blockchain platform. In 2018 IEEE 26th international symposium on modeling, analysis, and simulation of computer and telecommunication systems (MASCOTS) 2018 Sep 25 (pp. 264–276). IEEE. Available from: https://api.semanticscholar.org/CorpusID:44088292
Sirur S, Nurse JR, Webb H. Are we there yet? Understanding the challenges faced in complying with the General Data Protection Regulation (GDPR). In Proceedings of the 2nd international workshop on multimedia privacy and security 2018 Jan 15 (pp. 88–95). Available from: https://arxiv.org/abs/1808.07338v1
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. Int J Logist Res Appl. 2023 Nov 2;26(11):1498–535. https://doi.org/10.1080/13675567.2022.2088707
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. Ind Manag Data Syst. 2023 Feb 3;123(1):229–52. https://doi.org/10.1108/IMDS-12-2021-0784
Bag S, Rahman MS, Gupta S, Wood LC. Understanding and predicting the determinants of blockchain technology adoption and SMEs’ performance. Int J Logist Manag. 2023 Dec 1;34(6):1781–807. https://doi.org/10.1108/IJLM-01-2022-0017
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. Ann Oper Res. 2023 Aug;327(1):575–600. https://doi.org/10.1007/s10479-021-04129-6
Jum’a L. The role of blockchain-enabled supply chain applications in improving supply chain performance: the case of Jordanian manufacturing sector. Manag Res Rev. 2023 Jan 31. https://doi.org/10.1108/MRR-04-2022-0298
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. Manag Environ Qual. 2023 May 17;34(4):1111–28. https://doi.org/10.1108/MEQ-02-2022-0025
Li G, Xue J, Li N, Ivanov D. Blockchain-supported business model design, supply chain resilience, and firm performance. Transp Res E: Logist Transp Rev. 2022 Jul 1;163:102773. https://doi.org/10.1016/j.tre.2022.102773
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Lipsa Sadath, MSc, MCA, Deepti Mehrotra, PhD, Anand Kumar, PhD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright of their work, with first publication rights granted to Blockchain in Healthcare Today (BHTY). Read the full Copyright Statement.