A Novel Technique To Access Sensitive Medical Data With Access Policies

  • Dega Sunil Kumar Kumar
  • Pamidi Venkata Sreekanth Reddy
  • Madala Teja
  • Bestha Aruna Sai
  • Dr. Suresh Kumar
Keywords: privacy; healthcare data; security; Flexible; optimisation

Abstract

Ensuring the efficient and secure access of sensitive health records is one of the main issues challenging healthcare systems. This work offers a novel method that combines the Harmony Search Algorithm (HSA) with Attribute-Based Encryption (ABE) to provide strict data security, patient privacy, and robust access controls. Inspired by the evolution of musical harmony, the Harmony Search Algorithm successfully integrates ABE fundamentals to create and enhance controls on access that manage the retrieval of personal medical records. A dynamic framework is managed through this association, whereby HSA optimizes the development and growth of access controls and ABE presents a fine-grained, attribute-based method to encrypting and decrypting sensitive data.

This creative approach makes use of the HSA's ability to adapt access rules continuously to meet changing legal requirements and healthcare needs. The ABE algorithm offers local management of data access through making sure that only allowed entities with the necessary features can decode specific medical information, which enhances data security. With a primary focus on ensuring legal compliance, the framework's development was influenced by tight healthcare data laws, patient confidentiality, and ethical values. The recommended methodology offers an optimal combination of data security concepts and efficiency methods, representing an important progress in the domain of medical data management. This method integrates HSA and ABE to provide a framework that is safe, flexible and responsible for obtaining private medical information. This will maintain the security of patients and safety while expanding data useful for specified organizations.

References

[1] M. Li, S. Yu, K. Ren, and W. Lou, “Securing personal health records in cloud computing: Patient-centric and fine-grained data access control in multi-owner settings,” in Security and Privacy in Communication Networks. Springer, pp. 89–106, (2016).
[2] A. M.-H. Kuo, “Opportunities and challenges of cloud computing to improve health care services,” Journal of medical Internet research, vol. 13, no. 3, (2017).
[3] M. Li, S. Yu, Y. Zheng, K. Ren, and W. Lou, “Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 1, pp. 131–143, (2018).
[4] L. M. Vaquero, L. Rodero-Merino, J. Caceres, and M. Lindner, “A break in the clouds: towards a cloud definition,” ACM SIGCOMM Computer Communication Review, vol. 39, no. 1, pp. 50–55, (2019).
[5] H. Liang, L. X. Cai, D. Huang, X. Shen, and D. Peng, “An smdpbased service model for interdomain resource allocation in mobile cloud networks,” IEEE Transactions on Vehicular Technology, vol. 61, no. 5, pp. 2222–2232, (2019).
[6] M. M. Mahmoud and X. Shen, “A cloud-based scheme for protecting source-location privacy against hotspot-locating attack in wireless sensor networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 10, pp. 1805–1818, (2019).
[7] Q. Shen, X. Liang, X. Shen, X. Lin, and H. Luo, “Exploiting geodistributed clouds for e-health monitoring system with minimum service delay and privacy preservation,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 2, pp. 430–439, (2020).
[8] C. Wang, N. Cao, K. Ren, and W. Lou, “Enabling secure and efficient ranked keyword search over outsourced cloud data,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 8, pp. 1467–1479, (2021).
[9] W. Sun, B. Wang, N. Cao, M. Li, W. Lou, Y. T. Hou, and H. Li, “Verifiable privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 11, pp. 3025–3035, (2021).
[10] J. Yu, P. Lu, Y. Zhu, G. Xue, and M. Li, “Towards secure multikeyword top-k retrieval over encrypted cloud data,” IEEE Transactions on Dependable and Secure Computing, vol. 10, no. 4, pp. 239–250, (2022).
[11] Le, D.-N., Parvathy, V.S., Gupta, D., Khanna, A., Rodrigues, J.J.P.C., Shankar, K.” IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification” International Journal of Machine Learning and Cybernetics, vol. 12, no. 11, pp. 3235-3248, (2021).
[12]Elhoseny, M., Bian, G.-B., Lakshmanaprabu, S.K., Shankar, K., Singh, A.K., Wu, W.” Effective features to classify ovarian cancer data in internet of medical things” Computer Networks, vol. 159, pp. 147-156, (2019).
Published
2024-04-30
How to Cite
Kumar, D. S. K., Reddy, P. V. S., Teja, M., Sai, B. A., & Kumar, D. S. (2024). A Novel Technique To Access Sensitive Medical Data With Access Policies. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 10(1), 24-27. https://doi.org/10.33130/AJCT.2024v10i01.005

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.