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Preventing Forgeries by Securing Healthcare Data Using Blockchain Technology

  • V. VetriselviEmail author
  • Sridharan Pragatheeswaran
  • Varatharajan Thirunavukkarasu
  • Amaithi Rajan Arun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 933)

Abstract

Decentralization has gained a lot of attention due to its application in diverse fields. It is pioneered largely by bitcoin, a blockchain technology, and a financial application of decentralization, which has impacted a lot on how financial transactions happen in a secure manner. The advantage of using this technology is that there is no central authority to rely on. Thus, a decentralized storage of medical records would allow forgeries on the records to be reduced. We propose a solution to avoid forgery in healthcare sector using blockchain. The blockchain network in the proposed system will time-stamp and store healthcare management data and its associated files in the network storage. The network is decentralized; thus, the data is inherently secure. Yet this approach may create a storage exploitation and may lead to breakdown of the system. However, a machine learning-based classification model is used to decide upon which records that get into the blockchain to reduce the required storage. Hence, a system to securely store healthcare data using blockchain technology can be implemented or created.

Keywords

Decentralization Blockchain Healthcare Medical records Classification Machine learning 

References

  1. 1.
    Owenson, G., Dennis, R., Aziz, B.: A temporal blockchain: a formal analysis. In: International Conference on Collaboration Technologies and Systems, pp. 430–437 (2016)Google Scholar
  2. 2.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf (2008)
  3. 3.
    Bitcoin: Bitcoin developer guide. https://bitcoin.org/en/developer-guide#block-chain (2017)
  4. 4.
    Pattanayak, P., Verma, S., Crosby, M., Nachiappan, Kalyanaraman, V.: Blockchain technology: beyond bitcoin. http://scet.berkeley.edu/wpcontent/uploads/AIR-2016-Blockchain.pdf (2016)
  5. 5.
    Tschorsch, F., Scheuermann, B.: Bitcoin and beyond: a technical survey on decentralized digital currencies. IEEE Commun. Surv. Tutor. (2015)Google Scholar
  6. 6.
    Christidis, K., Devetsikiotis, M.: Blockchains and smart contracts for the internet of things. IEEE Access J. Rapid Open Access Publ. 04, 2292–2303 (2016)Google Scholar
  7. 7.
    Mizrahi, A.: A blockchain based property ownership recording system. https://chromaway.com/papers/A-blockchainbased-property-registry.pdf
  8. 8.
    Mettler, M.: Blockchain technology in healthcare the revolution starts here. In: IEEE 18th International Conference on e-Health Networking, Applications and Services (2017)Google Scholar
  9. 9.
    Estonian citizens will soon have the world’s most hack proof health-care records. https://qz.com/628889/thiseastern-european-country-is-moving-its-health-records-to-theblockchain/
  10. 10.
    Dokur, Z., Olmez, T.: Heart sound classification using wavelet transform and incremental self-organizing map. Digital Signal Proc. 18, 951–959 (2008). ElsevierCrossRefGoogle Scholar
  11. 11.
    Shervegar, M.V., Bhat, G.V.: Automatic segmentation of phonocardiogram using the occurrence of the cardiac events. Inf. Med. Unlock. J. 9, 6–10 (2017). ElsevierCrossRefGoogle Scholar
  12. 12.
    Azman, A., Jantan, A., Safara, F., Doraisamy, S., Ranga, A., Ramaiah, A.: Multi-level basis selection of wavelet packet decomposition tree for heart sound classification. Comput. Biol. Med. 43, 1407–1414 (2013). ElsevierCrossRefGoogle Scholar
  13. 13.
    Wada, T.: 64 point fast fourier transform circuit. http://www.ie.u-ryukyu.ac.jp/wada/design07/spece.html (2006)
  14. 14.
  15. 15.
    Grzegorczyk, I., Soliński, M., Łepek, M., Perka, A., Rosiński, J., Rymko, J., Stępień, K., Gierałtowski, J.: PCG classification using a neural network approach. In: 2016 Computing in Cardiology Conference (CinC), pp. 621–624. IEEE (2016)Google Scholar
  16. 16.
    Nassralla, M., El Zein, Z., Hajj, H.: Classification of normal and abnormal heart sounds. In: 2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME), pp. 1–4. IEEE (2017)Google Scholar
  17. 17.
    Potes, C., Parvaneh, S., Rahman, A., Conroy, B.: Ensemble of feature based and deep learning-based classifiers for detection of abnormal heart sounds. In: 2016 Computing in Cardiology Conference (CinC), pp. 621–624. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • V. Vetriselvi
    • 1
    Email author
  • Sridharan Pragatheeswaran
    • 1
  • Varatharajan Thirunavukkarasu
    • 1
  • Amaithi Rajan Arun
    • 1
  1. 1.Department of Computer Science and EngineeringCollege of Engineering, GuindyChennaiIndia

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