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Privacy Preservation-Based Access Control Intelligence for Cloud Data Storage in Smart Healthcare Infrastructure

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Abstract

Cloud service providers emphasize the concept of virtual machines, wherein resources and services are accessed over a distributed network system. The loss of user privacy is indeed contempt in concern to any individual or organization, particularly concerning the Healthcare infrastructure. This research paper introduces a Euclidean L3P-based Multi-Objective Successive Approximation (EMSA) algorithm, an efficient measure of privacy in the Healthcare Cloud. The role-based encryption keys are the critical foundation for the storage of sensitive data in cloud environments is presented here. The proposed EMSA algorithm is the hybridization of the existing Euclidean L3P Distance algorithm, Multi-objective Optimization algorithm, and Successive Approximation Iterative Proximate algorithm. In addition, the performance of the proposed EMSA compared with Bat, PUBAT, TPNGS, WOA, and CIC-WOA algorithms based on performance metrics, such as fitness, privacy, and utility. The simulation shows that, comparison to the existing state-of-the-art algorithms, the proposed EMSA model achieves higher privacy values of 0.34, 0.42, 0.42, 0.35, and 0.30.

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References

  1. Agarkhed, J., Deshpande, A., & Saraf, A. (2020). Data De-duplication Scheme for File Checksum in Cloud. Social Networking and Computational Intelligence (pp. 105–117). Singapore: Springer.

    Chapter  Google Scholar 

  2. Do, K., Tran, T. and Venkatesh, S., (2016) Multilevel anomaly detection for mixed data. arXiv preprint

  3. Do, K., Tran, T., & Venkatesh, S. (2018). Energy-based anomaly detection for mixed data. Knowledge and Information Systems, 57(2), 413–435.

    Article  Google Scholar 

  4. Fang, C., Guo, Y., Wang, N., & Ju, A. (2020). Highly efficient federated learning with strong privacy preservation in cloud computing. Computers & Security, 96, 101889.

    Article  Google Scholar 

  5. Gomathi, N., & Karlekar, N. P. (2019). Ontology and Hybrid Optimization Based SVNN for Privacy Preserved Medical Data Classification in Cloud. International Journal on Artificial Intelligence Tools, 28(03), 1950009.

    Article  Google Scholar 

  6. Guerrero, C., Lera, I., & Juiz, C. (2018). Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. Journal of Grid Computing, 16(1), 113–135.

    Article  Google Scholar 

  7. Han, S., Lin, J., Zhao, S., Xu, G., Ren, S., He, D., et al. (2020). Location Privacy-Preserving Distance Computation for Spatial Crowdsourcing. IEEE Internet of Things Journal, 7(8), 7550–7575.

    Article  Google Scholar 

  8. Kaaniche, N., & Laurent, M. (2017). Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms. Computer Communications, 111, 120–141.

    Article  Google Scholar 

  9. Karlekar, N. P., & Gomathi, N. (2017). Kronecker product and bat algorithm-based coefficient generation for privacy protection on cloud. International Journal of Modeling, Simulation, and Scientific Computing, 8(03), 1750021.

    Article  Google Scholar 

  10. Kundalwal, M. K., Chatterjee, K., & Singh, A. (2019). An improved privacy preservation technique in health-cloud. ICT Express, 5(3), 167–172.

    Article  Google Scholar 

  11. Li, C. T., Shih, D. H., & Wang, C. C. (2018). Cloud-assisted mutual authentication and privacy preservation protocol for telecare medical information systems. Computer methods and programs in biomedicine, 157, 191–203.

    Article  Google Scholar 

  12. Li, K., & Wang, J. (2017). Multi-objective optimization for cloud task scheduling based on the ANP model. Chinese Journal of Electronics, 26(5), 889–898.

    Article  Google Scholar 

  13. Midya, S., Roy, A., Majumder, K., & Phadikar, S. (2018). Multi-objective optimization technique for resource allocation and task scheduling in vehicular cloud architecture: A hybrid adaptive nature inspired approach. Journal of Network and Computer Applications, 103, 58–84.

    Article  Google Scholar 

  14. Revathi, S. T., Ramaraj, N., & Chithra, S. (2019). Brain storm-based Whale Optimization Algorithm for privacy-protected data publishing in cloud computing. Cluster Computing, 22(2), 3521–3530.

    Article  Google Scholar 

  15. Shivanna, K., Deva, S. P., & Santoshkumar, M. (2017). Privacy preservation in cloud computing with double encryption method. Computer Communication Networking and Internet Security (pp. 125–133). Singapore: Springer.

    Chapter  Google Scholar 

  16. Subramaniyam, A., Mahapatra, R. P., & Singh, P. (2019). Taylor and Gradient Descent-Based Actor Critic Neural Network for the Classification of Privacy Preserved Medical Data. Big data, 7(3), 176–191.

    Article  Google Scholar 

  17. Wang, X., Bai, L., Yang, Q., Wang, L., & Jiang, F. (2019). A dual privacy-preservation scheme for cloud-based eHealth systems. Journal of Information Security and Applications, 47, 132–138.

    Article  Google Scholar 

  18. Xiao, Z., Liu, G., He, D., Guo, Y., & Du, J. (2020). Concurrent request multiplexing for cloud composite service reservation. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2020.2967379.

    Article  Google Scholar 

  19. Zhang, G., Liu, X., & Yang, Y. (2014). Time-series pattern based effective noise generation for privacy protection on cloud. IEEE Transactions on Computers, 64(5), 1456–1469.

    Article  MathSciNet  Google Scholar 

  20. Zhang, G., Yang, Y. and Chen, J., 2013, July. A privacy-leakage-tolerance based noise enhancing strategy for privacy protection in cloud computing. In 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (pp. 1–8). IEEE.

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Funding

All sources of funding for the research work and their role in the design of the study and collection, analysis, interpretation of data, and in writing the manuscript should be declared.

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Authors and Affiliations

  1. Department of Information Technology, Rajalakshmi Engineering College, Chennai, Tamil Nadu, 602105, India

    A. Sathya

  2. Easwari Engineering College, Chennai, Tamil Nadu, 600089, India

    S. Kanaga Suba Raja

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  1. A. Sathya
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  2. S. Kanaga Suba Raja
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Correspondence to A. Sathya.

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Sathya, A., Raja, S.K.S. Privacy Preservation-Based Access Control Intelligence for Cloud Data Storage in Smart Healthcare Infrastructure. Wireless Pers Commun 118, 3595–3614 (2021). https://doi.org/10.1007/s11277-021-08278-6

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