A Novel deep learning framework for improving the quality of services using block chain technology

Document Type : Research Paper


1 Department of CSE, IFET Colleg of Engineering, Villupuram, India.

2 Department of IT, Annamalai University, Chidambaram, India.


Electronic Health Record (EHR) holds immensely sensitive information and consisting of ‎crucial data related to the patients.  Storing and organizing such data is highly arduous task.  ‎Researches still going on to improve the Quality of Service (QOS) of such data. Existing ‎study focused only on improving the system throughput, privacy and latency issues.  But they ‎did not tend to scrutinize the scalability and privacy of such data records.  In this paper, we ‎propose a novel deep learning framework to improve the Quality of Service using block chain ‎technology.  Initially the data is classified into high priority and low priority based on its ‎nature by using Recurrent Neural Network (RNN).  Then, the classified high priority data is ‎further allowed to each block of a block chain and the low priority data is stored and ‎maintained as log file. Finally, the results are compared based on the evaluation metrics ‎which demonstrates our proposed novel deep learning framework achieves better accuracy.


[1] N. Z. Aitzhan and D. Svetinovic, Security and privacy in decentralized energy trading through multi-signatures,
blockchain and anonymous messaging streams, IEEE Trans. Dependable Secure Comput., 15(5), Sep./Oct. 2018,
pp. 840-852.
[2] M. Alhussein and G. Muhammad, Voice pathology detection using deep learning on mobile healthcare framework,
IEEE Access, 6, 2018, pp. 41034-41041.
[3] R. Amin, S. H. Islam, G. P. Biswas, M. K. Khan and N. Kumar, An efficient and practical smart card based
anonymity preserving user authentication scheme for tmis using elliptic curve cryptography, Journal of Medical
Systems, 39, Oct. 2015, pp. 1-18.
[4] G. S. Aujla, R. Chaudhary, K. Kaur, S. Garg, N. Kumar and R. Ranjan, Safe: Sdn-assisted framework for
edge-cloud interplay in secure healthcare ecosystem, IEEE Transactions on Industrial Informatics, 15(1), 2019, pp.
[5] H. Bao and R. Lu, Comment on privacy-enhanced data aggregation scheme against internal attackers in smart
grid, IEEE Transactions on Industrial Informatics, 12(1), 2016, pp. 2-5.
[6] P. Bhattacharya, S. Tanwar, U. Bodke, S. Tyagi and N. Kumar, Bindaas: Blockchain-based deep-learning as-aservice in healthcare 4.0 applications. IEEE Transactions on Network Science and Engineering, 2019.
[7] P. Bhattacharya, S. Tanwar, R. Shah and A. Ladha, Mobile edge computing-enabled blockchain framework-a survey,
in Proceedings of ICRIC 2019 (P. K. Singh, A. K. Kar, Y. Singh, M. H. Kolekar and S. Tanwar, eds.), Springer
International Publishing, (Cham), 2020, pp. 797-809.
[8] L. Chen, W. K. Lee, C. C. Chang, K. K. R. Choo and N. Zhang, Blockchain based searchable encryption for
electronic health record sharing, Future Generation Computer Systems, 95, 2019, pp. 420 -429.
[9] C. Esposito, A. De Santis, G. Tortora, H. Chang and K. K. R. Choo, Blockchain: A panacea for healthcare
cloudbased data security and privacy, IEEE Cloud Computing, 5(1), 2018, pp. 31-37.
[10] M. A. Ferrag, M. Derdour, M. Mukherjee, A. Derhab, L. Maglaras, and H. Janicke, Blockchain technologies for
the Internet of Things: Research issues and challenges, IEEE Internet Things J., 6(2), Apr. 2019, pp. 2188-2204.
[11] I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016.
[12] P. Gope, R. Amin, S. H. Islam, N. Kumar and V. K. Bhalla, Lightweight and privacy-preserving rfid authentication scheme for distributed iot infrastructure with secure localization services for smart city environment, Future
Generation Computer Systems, 83, 2018, pp. 629 - 637.
[13] R. Gravina, P. Alinia, H. Ghasemzadeh and G. Fortino, Multi-sensor fusion in body sensor networks: State-ofthe-art and research challenges, Information Fusion, 35, 2017, pp. 68- 80.
[14] Z. Guan et al., Privacy-preserving and efficient aggregation based on blockchain for power grid communications
in smart communities, IEEE Commun. Mag., 56(7), Jul. 2018, pp. 82-88.
[15] J. J. Hathaliya, S. Tanwar, S. Tyagi and N. Kumar, Securing electronics healthcare records in healthcare 4.0: A
biometric-based approach, Computers & Electrical Engineering, 76, 2019, pp. 398-410.
[16] O. Jacobson and H. Dalianis, Applying deep learning on electronic health records in Swedish to predict healthcare
associatsed infections, In Proceedings of the 15th Workshop on Biomedical Natural Language Processing, (Berlin,
Germany), Association for Computational Linguistics, Aug. 2016, pp. 191-195.
[17] N. Kabra, P. Bhattacharya, S. Tanwar and S. Tyagi, Mudrachain: Blockchain-based framework for automated
cheque clearance in financial institutions, Future Generation Computer Systems, 102, 2020, pp. 574-587.
[18] H. J. Kim and H. S. Kim, Auth hotp-hotp based authentication scheme over home network environment, In
International Conference on Computational Science and Its Applications Santander, Spain, Springer, 2011, pp.
[19] X. Li, M. H. Ibrahim, S. Kumari, A. K. Sangaiah, V. Gupta and K. K. R. Choo, Anonymous mutual authentication
and key agreement scheme for wearable sensors in wireless body area networks, Computer Networks, 129, 2017, pp.
[20] Z. Li, J. Kang, R. Yu, D. Ye, Q. Deng and Y. Zhang, Consortium blockchain for secure energy trading in industrial
Internet of Things, IEEE Trans. Ind. Inform., 14(8), Aug. 2018, pp. 3690-3700.
[21] Y. Liu, T. Ge, K. Mathews, H. Ji and D. McGuinness, Exploiting task-oriented resources to learn word embeddings
for clinical abbreviation expansion, In Proceedings of BioNLP, (Beijing, China), Association for Computational
Linguistics, 15, July 2015, pp. 92-97.
[22] T. Pham, T. Tran, D. Phung and S. Venkatesh, Deepcare: A deep dynamic memory model for predictive medicine,
In Pacific-Asia Conference on Knowledge Discovery and Data Mining, Macau, China, Springer, 2016, pp. 30-41.
[23] C. Pop, T. Cioara, M. Antal, I. Anghel, I. Salomie and M. Bertoncini, Blockchain based decentralized management of demand response programs in smart energy grids, Sensors, 18(1), 2018, p. 162. [Online]. Available:
https://www.mdpi.com/1424-8220/18/1/162.[24] A. Srivastava, P. Bhattacharya, A. Singh, A. Mathur, O. Prakash and R. Pradhan, A distributed credit transfer
educational framework based on blockchain, In 2018 Second International Conference on Advances in Computing,
Control and Communication Technology (IAC3T), Allahabad, India, IEEE, 2018, pp. 54-59.
[25] S. Srivastava and S. Lessmann, A comparative study of LSTM neural networks in forecasting day-ahead global
horizontal irradiance with satellite data, Sol. Energy, 162, Mar. 2018, pp. 232-247.
[26] The essential eight technologies board byte: Blockchain, Accessed: 30, Apr. 2019. [Online]. Available:
https://www.pwc.com.au/pdf/essential-emerging-technologies-blockchain .pdf.
[27] J. S. Weng, J. Weng, M. Li, Y. Zhang and W. Luo, Deepchain: Auditable and privacy-preserving deep learning
with blockchain-based incentive, IACR Cryptology ePrint Archive, 14, 2019, pp. 1-18.
[28] T. K. Whangbo, S. J. Eun, E. Y. Jung, D. K. Park, S. J. Kim and C. H. Kim et al., Personalized urination
activity recognition based on a recurrent neural network using smart band, Int. Neurourology J., 22(Suppl 2), Jul.
2018, pp. 91-100.
[29] R. Yang, F. R. Yu, P. Si, Z. Yang and Y. Zhang, Integrated blockchain and edge computing systems: A survey,
some research issues and challenges, IEEE Commun. Surveys Tut., 21(2), Apr./Jun. 2019, pp. 1508-1532.
[30] P. Zhang, J. White, D. C. Schmidt, G. Lenz and S. T. Rosenbloom, Fhirchain: applying blockchain to securely
and scalably share clinical data, Computational and structural biotechnology journal, 16, 2018, pp. 267-278.
Volume 12, Issue 2
November 2021
Pages 1513-1529
  • Receive Date: 05 April 2021
  • Revise Date: 14 May 2021
  • Accept Date: 20 June 2021