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Securing the medical data using enhanced privacy preserving based blockchain technology in Internet of Things

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Abstract

Now that we live in the digital age, the proliferation of Internet of Things (IoT) strategies raises various design concerns related to privacy for businesses. Patients’ medical data poses an ethical and legal quandary for healthcare organisations, making security a difficult problem to solve. Early studies suggest that blockchain technology could be a substantial answer to the IoT’s data security issues. It is therefore critical to ensure data security when designing a blockchain approach for healthcare applications. A blockchain-based data broadcast strategy with a categorization model in the healthcare industry is proposed in this study. Data from multiple IoT data providers, like blockchain, is used to make secure training algorithms. To ensure a safe and secure learning environment, Homomorphic Encryption (HE) technology is used. The oppositional-based harmony search (OHS) algorithm was used to make the best key for the HE algorithm. For the group of multiple shares of acquired images, a multiple-share creation (MSC) ideal is used. This gives privacy and security to the model. In addition, the blockchain technology is used to transmit data securely to the cloud server, which performs the classification model based on the convolutional neural network to determine the presence of disease. To summarise, the proposed model is known as the OHE-MSC based network, and it employs blockchain technology to create a secure and trustworthy platform for the exchange of data between many data providers using IoT and logging it in a shared ledger. Two benchmark datasets, such as the Breast Cancer Wisconsin Dataset (BCWD) and the Heart Disease Data Set (HDD), are used to evaluate the projected method's performance. Several parts of the simulation showed that the OHE-MSC-based network model achieved better performance than all other approaches.

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References

  1. Baker, T., Asim, M., Tawfik, H., Aldawsari, B., Buyya, R.: An energy-aware service composition algorithm for multiple cloudbased IoT applications. J. Netw. Comput. Appl. 89, 96–108 (2017)

    Article  Google Scholar 

  2. By, Gartner Says, 2020, More Than Half of Major New Business Processes and Systems Will Incorporate Some Element of the Internet of Things, Publicado em Janeiro (2016)

  3. Hammi, B., Khatoun, R., Zeadally, S., Fayad, A., Khoukhi, L.: IoT technologies for smart cities. IET Net. 7, 1–13 (2017)

    Google Scholar 

  4. Alkheir, A.A., Aloqaily, M., Mouftah, H.T.: Connected and autonomous electric vehicles (caevs). IT Professional 20, 54–61 (2018)

    Article  Google Scholar 

  5. Lee, In., Lee, K.: The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Bus. Horiz. 58, 431–440 (2015)

    Article  Google Scholar 

  6. Tonyali, S., Akkaya, K., Saputro, N., Uluagac, A.S., Nojoumian, M.: Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled smart metering systems. Future Generation Comp. Syst. 78, 547–557 (2018)

    Article  Google Scholar 

  7. Nawir, Mukrimah and Amir, Amiza and Yaakob, Naimah and Lynn, Ong Bi, Internet of Things (IoT): Taxonomy of security attacks, 2016 3rd International Conference on Electronic Design (ICED), 321–326 (2016)

  8. Liu, Bin and Yu, Xiao Liang and Chen, Shiping and Xu, Xiwei and Zhu, Blockchain based data integrity service framework for IoT data, Liming, 2017 IEEE International Conference on Web Services (ICWS), 468–475 (2017)

  9. Anirudh, M and Thileeban, S Arul and Nallathambi, Daniel Jeswin, Use of honeypots for mitigating DoS attacks targeted on IoT networks, 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), 1–4 (2017)

  10. Li, F., Xiong, P.: Practical secure communication for integrating wireless sensor networks into the internet of things. IEEE Sens. J. 13, 3677–3684 (2013)

    Article  Google Scholar 

  11. Muhammad, K., Hamza, R., Ahmad, J., Lloret, J., Wang, H., Baik, S.W.: Secure surveillance framework for IoT systems using probabilistic image encryption. IEEE Trans. Industr. Inf. 14, 3679–3689 (2018)

    Article  Google Scholar 

  12. Won, Jongho and Seo, Seung-Hyun and Bertino, Elisa, A secure communication protocol for drones and smart objects, Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security, 249–260 (2015)

  13. Aloqaily, M., Ridhawi, A., Ismaeel, S., Bany, H., Jararweh, Y.: Data and service management in densely crowded environments: challenges, opportunities, and recent developments. IEEE Commun. Mag. 57, 81–87 (2019)

    Article  Google Scholar 

  14. Baker, Thar and Asim, Muhammad and MacDermott, Aine and Iqbal, Farkhund and Kamoun, Faouzi and Shah, Babar and Alfandi, Omar and Hammoudeh, Mohammad, A secure fog-based platform for SCADA-based IoT critical infrastructure, Software: Practice and Experience (2019)

  15. Rathee, G., Sharma, A., Iqbal, R., Aloqaily, M., Jaglan, N., Kumar, R.: A blockchain framework for securing connected and autonomous vehicles. Sensors 14, 3165 (2019)

    Article  Google Scholar 

  16. Ismaeel, A.R., Aloqaily, M.K., Jararweh, Y., Baker, Y.T.: A profitable and energy-efficient cooperative fog solution for IoT services. IEEE Trans. Industrial Inform. 16, 3578 (2019)

    Google Scholar 

  17. Ahmad, A., Din, S.P., Jeon, A., Aloqaily, G., Ahmad Mudassar, M.: Real-time route planning and data dissemination for urban scenarios using the internet of things. IEEE Wireless Commun. 26, 50–55 (2019)

    Article  Google Scholar 

  18. Hammi, M.T., Hammi, B.B., Serhrouchni Ahmed, P.: Bubbles of trust: a decentralized blockchain-based authentication system for IoT. Comp. & Security 78, 126 (2018)

    Article  Google Scholar 

  19. Srinivasu, P.N., Bhoi, A.K., Nayak, S.R., Bhutta, M.R., Woźniak, M.: Blockchain technology for secured healthcare data communication among the non-terminal nodes in IoT architecture in 5G network. Electronics 10(12), 1437 (2021)

    Article  Google Scholar 

  20. Sharma, P., Moparthi, N.R., Namasudra, S., Shanmuganathan, V., Hsu, C.H.: Blockchain-based IoT architecture to secure healthcare system using identity-based encryption. Exp. Syst. (2021). https://doi.org/10.1111/exsy.12915

    Article  Google Scholar 

  21. Chen, W., Zhu, S., Li, J., Wu, J., Chen, C.L., Deng, Y.Y.: Authorized shared electronic medical record system with proxy Re-encryption and blockchain technology. Sensors 21(22), 7765 (2021)

    Article  Google Scholar 

  22. Ali, A., Almaiah, M.A., Hajjej, F., Pasha, M.F., Fang, O.H., Khan, R., Teo, J., Zakarya, M.: An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network. Sensors 22(2), 572 (2022)

    Article  Google Scholar 

  23. Doss, S., Paranthaman, J., Gopalakrishnan, S., Duraisamy, A., Pal, S., Duraisamy, B., Le, D.N.: Memetic optimization with cryptographic encryption for secure medical data transmission in IoT-based distributed systems. Comp., Mater. & Continua 66(2), 1577–1594 (2021)

    Article  Google Scholar 

  24. Gupta, B.B., Li, K.C., Leung, V.C., Psannis, K.E., Yamaguchi, S.: Blockchain-assisted secure fine-grained searchable encryption for a cloud-based healthcare cyber-physical system. IEEE/CAA J. Automatica Sinica 8(12), 1877–1890 (2021)

    Article  Google Scholar 

  25. Yang, X., Li, T., Pei, X., Wen, L., Wang, C.: Medical data sharing scheme based on attribute cryptosystem and blockchain technology. IEEE Access 8, 45468–45476 (2020)

    Article  Google Scholar 

  26. Gheisari, M., Najafabadi, H.E., Alzubi, J.A., Gao, J., Wang, G., Abbasi, A.A., Castiglione, A.: OBPP: An ontology-based framework for privacy-preserving in IoT-based smart city. Futur. Gener. Comput. Syst. 1(123), 1–3 (2021)

    Article  Google Scholar 

  27. Movassagh, A.A., Alzubi, J.A., Gheisari, M., Rahimi, M., Mohan, S., Abbasi, A.A., Nabipour, N.: Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J. Ambient. Intell. Humaniz. Comput. 27, 1–9 (2021)

    Google Scholar 

  28. Alzubi, O.A., Alzubi, J.A., Shankar, K., Gupta, D.: Blockchain and artificial intelligence enabled privacy-preserving medical data transmission in Internet of Things. Trans. Emerging Telecommun. Technol. 32(12), e4360 (2021)

    Article  Google Scholar 

  29. Alzubi, J.A., Alzubi, O.A., Singh, A., Ramachandran, M.: Cloud-IIoT-based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning. IEEE Trans. Industr. Inf. 19(1), 1080–1087 (2022)

    Article  Google Scholar 

  30. Shankar, K., Eswaran, P.: RGB based multiple share creation in visual cryptography with aid of elliptic curve cryptography. China Commun. 14(2), 118–130 (2017)

    Article  Google Scholar 

  31. Gupta, B.B., Quamara, M.: An overview of internet of things (iot): architectural aspects, challenges, and protocols. Concurr. Comput. Practice Exper. 32(21), e4946 (2020)

    Article  Google Scholar 

  32. Kathamuthu, N.D., Chinnamuthu, A., Iruthayanathan, N., Ramachandran, M., Gandomi, A.H.: Deep Q-learning-based neural network with privacy preservation method for secure data transmission in internet of things (IoT) healthcare application. Electronics 11(1), 157 (2022)

    Article  Google Scholar 

  33. Khayyat, M.M., Khayyat, M.M., Abdel-Khalek, S., Mansour, R.F.: Blockchain enabled optimal hopfield chaotic neural network based secure encryption technique for industrial internet of things environment. Alexandria Eng. J. 61(12), 11377 (2022)

    Article  Google Scholar 

  34. Mansour, R.F., Parah, S.A.: Reversible data hiding for electronic patient information security for telemedicine applications. Arab J. Sci. Eng. 46, 9129–9144 (2021). https://doi.org/10.1007/s13369-021-05716-2

    Article  Google Scholar 

  35. Gull, S., Mansour, R.F., Aljehane, N.O., et al.: A self-embedding technique for tamper detection and localization of medical images for smart-health. Multimed. Tools Appl. 80, 29939–29964 (2021). https://doi.org/10.1007/s11042-021-11170-x

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Vijayawada, India

    Ramesh Vatambeti

  2. Department of Computer Science and Engineering, GITAM School of Technology, GITAM University-Bengaluru Campus, Bengaluru, India

    E. S. Phalguna Krishna & M. Ganesh Karthik

  3. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad Campus, Hyderabad, India

    Vijay Kumar Damera

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  1. Ramesh Vatambeti
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  2. E. S. Phalguna Krishna
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  3. M. Ganesh Karthik
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  4. Vijay Kumar Damera
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Contributions

RV: Literature review & proposed algorithm, ESPK & MGK: implementation, VKD: results & discussion.

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Correspondence to Ramesh Vatambeti.

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Vatambeti, R., Krishna, E.S.P., Karthik, M.G. et al. Securing the medical data using enhanced privacy preserving based blockchain technology in Internet of Things. Cluster Comput 27, 1625–1637 (2024). https://doi.org/10.1007/s10586-023-04056-0

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