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Designing a trivial information relaying scheme for assuring safety in mobile cloud computing environment

  • N. Thillaiarasu
  • S. Chenthur Pandian
  • V. Vijayakumar
  • S. Prabaharan
  • Logesh Ravi
  • V. SubramaniyaswamyEmail author
Article
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Abstract

Due to increased attraction in cloud computing, mobile devices could store or acquire private and confidential information from everywhere at any point in time. In parallel, the information safety issues over mobile computing become rigorous and retard increased advancements in the mobile cloud. Crucial analysis were performed to enhance the safety in cloud computing. Most of them are not appropriate for mobile cloud computing due to limited energy resource, thus mobile devices are unable to perform assessments and complex tasks. The crucial requirement of mobile cloud application is to provide solution with minimum computational overhead. Thus the aim of the research is to design a trivial information relaying scheme (TIRS) for mobile cloud computing. The proposed scheme implements Ciphertext Policy Attribute-based Encryption (CP-ABE) to alter the general framework of access governance hierarchy to make it appropriate for mobile cloud environment. The TIRS displaces immense segments of the assessment concentrated access governance hierarchy modifications in CP-ABE from smart devices to the peripheral proxy servers. Furthermore, TIRS initiates element portrayal field to plan indolent cancellation which is a thriving dispute for CP-ABE system. The experimental analysis depicts that TIRS successfully minimize the overheads during user relaying information over the mobile cloud environment.

Keywords

Mobile cloud Ciphertext policy-attribute based encryption Trivial information relaying scheme Energy Computational overheads Proxy servers 

Notes

References

  1. 1.
    Younas, M., Jawawi, D. N., Ghani, I., Fries, T., & Kazmi, R. (2018). Agile development in the cloud computing environment: A systematic review. Information and Software Technology.  https://doi.org/10.1016/j.infsof.2018.06.014.Google Scholar
  2. 2.
    Kim, J., & Chilamkurti, N. (2017). Advanced technologies and applications for future wireless and mobile communication systems. Wireless Personal Communications, 93(2), 285–286.CrossRefGoogle Scholar
  3. 3.
    Bagiwa, I. L., Ghani, I., Younas, M., & Bello, M. (2016). Systematic literature review on cloud adoption. International Journal of Internet, Broadcasting and Communication, 8(2), 1–22.Google Scholar
  4. 4.
    Arunkumar, S., Subramaniyaswamy, V., Vijayakumar, V., Chilamkurti, N., & Logesh, R. (2019). SVD-based robust image steganographic scheme using RIWT and DCT for secure transmission of medical images. Measurement, 139, 426–437.CrossRefGoogle Scholar
  5. 5.
    Benítez-Guerrero, E., Arabnia, H. R., Hashemi, R. R., Vert, G., Chen-namaneni, A., & Solo, A. M. G. (2010). Context-aware mobile information systems: Data management issues and opportunities. In IKE (pp. 127–133).Google Scholar
  6. 6.
    Motavaselalhagh, F., Esfahani, F. S., & Arabnia, H. R. (2015). Knowledge-based adaptable scheduler for SaaS providers in cloud computing. Human-centric Computing and Information Sciences, 5(1), 16.CrossRefGoogle Scholar
  7. 7.
    Ravi, L., Subramaniyaswamy, V., Devarajan, M., Ravichandran, K. S., Arunkumar, S., Indragandhi, V., et al. (2019). SECRECSY: A secure framework for enhanced privacy-preserving location recommendations in cloud environment. Wireless Personal Communications.  https://doi.org/10.1007/s11277-019-06500-0.Google Scholar
  8. 8.
    Ahmad, A., Paul, A., Khan, M., Jabbar, S., Rathore, M. M. U., Chilamkurti, N., et al. (2017). Energy efficient hierarchical resource management for mobile cloud computing. IEEE Transactions on Sustainable Computing, 2(2), 100–112.CrossRefGoogle Scholar
  9. 9.
    Kumari, R., Kaushal, S., & Chilamkurti, N. (2018). Energy conscious multi-site computation offloading for mobile cloud computing. Soft Computing, 22(20), 6751–6764.CrossRefGoogle Scholar
  10. 10.
    Fan, H., Hussain, F. K., Younas, M., & Hussain, O. K. (2015). An integrated personalization framework for SaaS-based cloud services. Future Generation Computer Systems, 53, 157–173.CrossRefGoogle Scholar
  11. 11.
    Nafea, I., & Younas, M. (2014). Improving the performance and reliability of mobile commerce in developing countries. In International conference on mobile web and information systems (pp. 114–125). Cham: Springer.Google Scholar
  12. 12.
    Shiraz, M., Gani, A., Khokhar, R., Rahman, A., Iftikhar, M., & Chilamkurti, N. (2017). A distributed and elastic application processing model for mobile cloud computing. Wireless Personal Communications, 95(4), 4403–4423.CrossRefGoogle Scholar
  13. 13.
    Kumar, N., Chilamkurti, N., Zeadally, S., & Jeong, Y. S. (2014). Achieving quality of service (QoS) using resource allocation and adaptive scheduling in cloud computing with grid support. The Computer Journal, 57(2), 281–290.CrossRefGoogle Scholar
  14. 14.
    Grønli, T. M., Ghinea, G., & Younas, M. (2014). Context-aware and automatic configuration of mobile devices in cloud-enabled ubiquitous computing. Personal and Ubiquitous Computing, 18(4), 883–894.CrossRefGoogle Scholar
  15. 15.
    Grønli, T. M., Ghinea, G., Younas, M., & Hansen, J. (2015). Automatic configuration of mobile applications using context-aware cloud-based services. In F. Xhafa, L. Barolli, A. Barolli, & P. Papajorgji (Eds.), Modeling and processing for next-generation big-data technologies (pp. 367–383). Cham: Springer.CrossRefGoogle Scholar
  16. 16.
    Ravi, L., Vairavasundaram, S., Palani, S., & Devarajan, M. (2019). Location-based personalized recommender system in the internet of cultural things. Journal of Intelligent & Fuzzy Systems, 36(5), 4141–4152.CrossRefGoogle Scholar
  17. 17.
    Qin, Z., Weng, J., Cui, Y., & Ren, K. (2018). Privacy-preserving image processing in the cloud. IEEE Cloud Computing, 5(2), 48–57.CrossRefGoogle Scholar
  18. 18.
    Geeta, C. M., Raghavendra, S., Buyya, R., & Venugopal, K. R. (2018). Data auditing and security in cloud computing: Issues, challenges and future directions. International Journal of Computer, 28(1), 8–57.Google Scholar
  19. 19.
    Bandal, P., Dhane, A., Chavan, S., & Nikam, N. (2018). Key exchange privacy preserving technique in cloud computing. International Research Journal of Engineering and Technology, 5(3), 3113–3117.Google Scholar
  20. 20.
    Farràs, O., Ribes-González, J., & Ricci, S. (2018). Privacy-preserving data splitting: A combinatorial approach. arXiv preprint arXiv:1801.05974.
  21. 21.
    Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X. Z., & Wang, G. G. (2019). Hybrid bio-inspired user clustering for the generation of diversified recommendations. Neural Computing and Applications.  https://doi.org/10.1007/s00521-019-04128-6.Google Scholar
  22. 22.
    Devarajan, M., Fatima, N. S., Vairavasundaram, S., & Ravi, L. (2019). Swarm intelligence clustering ensemble based point of interest recommendation for social cyber-physical systems. Journal of Intelligent & Fuzzy Systems, 36(5), 4349–4360.CrossRefGoogle Scholar
  23. 23.
    Ou, L., Yin, H., Qin, Z., Xiao, S., Yang, G., & Hu, Y. (2018). An efficient and privacy-preserving multiuser cloud-based lbs query scheme. Security and Communication Networks., 2018, 1–11.CrossRefGoogle Scholar
  24. 24.
    Chaudhary, S., & Joshi, N. K. (2018). Secured blended approach for cryptographic algorithm in cloud computing. International Journal of Pure and Applied Mathematics, 118(20), 297–304.Google Scholar
  25. 25.
    Liu, G., Yang, G., Wang, H., Dai, H., & Zhou, Q. (2018). QSDB: An encrypted database model for privacy-preserving in cloud computing. KSII Transactions on Internet & Information Systems, 12(7), 3375–3400.Google Scholar
  26. 26.
    Qi, L., Meng, S., Zhang, X., Wang, R., Xu, X., Zhou, Z., et al. (2018). An exception handling approach for privacy-preserving service recommendation failure in a cloud environment. Sensors, 18(7), 2037.CrossRefGoogle Scholar
  27. 27.
    Thillaiarasu, N., Pandian, S. C., Balaji, G. N., Shierly, R. B., Divya, A., & Prabha, G. D. (2018). Enforcing confidentiality and authentication over public cloud using hybrid cryptosystems. In International conference on intelligent data communication technologies and internet of things (pp. 1495–1503). Cham: Springer.Google Scholar
  28. 28.
    Thillaiarasu, N., & Chenthur Pandian, S. (2017). A novel scheme for safeguarding confidentiality in public clouds for service users of cloud computing. Cluster Computing.  https://doi.org/10.1007/s10586-017-1178-8.Google Scholar
  29. 29.
    Alshikhsaleh, M., & Zohdy, M. (2018). Privacy-preserving multi keyword search in cloud computing. International Journal of Advancements, Research, Innovations in Information Technology, 4(2), 1079–1084.Google Scholar
  30. 30.
    Gai, K., Qiu, M., & Zhao, H. (2017). Privacy-preserving data encryption strategy for big data in mobile cloud computing. IEEE Transactions on Big Data.  https://doi.org/10.1109/TBDATA.2017.2705807.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • N. Thillaiarasu
    • 1
  • S. Chenthur Pandian
    • 2
  • V. Vijayakumar
    • 3
  • S. Prabaharan
    • 4
  • Logesh Ravi
    • 5
  • V. Subramaniyaswamy
    • 6
    Email author
  1. 1.School of Computing Science and EngineeringGalgotias UniversityGreater NoidaIndia
  2. 2.SNS College of TechnologyCoimbatoreIndia
  3. 3.School of Computing Science and EngineeringVellore Institute of TechnologyChennaiIndia
  4. 4.Department of Computer Science and EngineeringJyothishmathi Institute of Technology and ScienceKarimnagarIndia
  5. 5.Sri Ramachandra Faculty of Engineering and TechnologySri Ramachandra Institute of Higher Education and ResearchChennaiIndia
  6. 6.School of ComputingSASTRA Deemed UniversityThanjavurIndia

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