Efficient Big Data-Based Access Control Mechanism for IoT Cloud Environments

  • Authors

    • Yoon-Su Jeong
    • Yong-Tae Kim
    • Gil-Cheol Park
    2018-12-13
    https://doi.org/10.14419/ijet.v7i4.39.25679
  • Access Control, Algorithm, IoT, Big Data, Cloud Service.
  • Background/Objectives: Recently, the data using in the internet had processed through the network every day, and cloud services related to IoT are increasing rapidly. In particular, the cloud service related to IoT has been transformed into an era in which data is generated and processed by an individual centered around the enterprise in the past. However, the use of mobile phones and IoT technology had diversified, and the demand for computational cost and accuracy has increased.

    Methods/Statistical analysis: we propose access control method based on big data that can process various attributes of data in block in IoT cloud environment. The proposed scheme aims to minimize the service latency of users by extracting the security parameters  of each data by attribute and converting them into pairs with polynomials. In the performance evaluation, we had fined that the data processing time was 7.2% higher than previous scheme and the data processing rate was 9.7% higher than previous scheme. The accuracy according to the type and size of the different data improved by 18.1%. IoT cloud server and user communication delay was 8.5% higher than previous scheme. Finally, the server overhead reduced by 5.8%.

    Findings: We propose a method that can access verified data without delaying the data by constructing data into subnets and then applying the security parameter  of the data constituting each subnet to -bit and applying it to the polynomial coefficients.

    Improvements/Applications: In future research, proposed scheme can be applied to various services related to large-scale data access in the cloud environment.

     

     

  • References

    1. [1] Kan Y, Xiaohua J. Kui R, Secure and Verifiable Policy Update Outsourcing for Big Data Access Control in the Cloud, IEEE Transactions on Parallel and Distributed Systems, 2015, 26(12), pp. 3461-3470.

      [2] Chen H S, Bhargava B, Fu Z C, Multiables-Based Scalable Access Control for Big Data Applications, IEEE Cloud Computing, 2014, 1(3), pp. 65-71.

      [3] Hu C Q, Li W, Cheng X. Yu J. Wang S, Bie R, A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds, IEEE Transactions on Big Data, 2018, 4(3), pp. 341-355.

      [4] Zeng W, Yang Y, Luo B, Access control for big data using data content, Proceedings of the 2013 IEEE International Conference on Big Data, 2013, pp.45-47.

      [5] Aris I B, Sahbusdin R K Z, Amin A F M, Impacts of IoT and big data to automotive industry, Proceedings of the 2015 10th Asian Control Conference, 2015, pp. 1-5.

      [6] Barreto, F M, Duarte, P A. de S. D, Maia M E F, Andrade R M. de C, Viana W, CoAP-CTX: A Context-Aware CoAP Extension for Smart Objects Discovery in Internet of Things, Proceedings of the 2017 IEEE 41st Annual Computer Software and Applications Conference, 2017, 1, pp. 575-584.

      [7] Wu C W, Lin F J, Wang C H, Chang N, OneM2M-based IoT protocol integration, 2017 IEEE Conference on Standards for Communications and Networking , 2017, pp. 252-257.

      [8] Amur H, Cipar J, Gupta V, Ganger G R, Kozuch M A, Schwan K, Robust and flexible power-proportional storage, Proceedings of the 1st ACM symposium on Cloud computing, 2010, pp. 217-228.

      [9] Merla P, Liang Y, Data analysis using Hadoop MapReduce environment, Proceedings of the 2017 IEEE International Conference on Big Data, 2017, pp. 4783-4785.

      [10] Strang K D, Sun Z, Meta-analysis of big data security and privacy: Scholarly literature gaps, Proceedings of the 2016 IEEE International Conference on Big Data, 2016, pp. 4035-4037.

      [11] Revathy P, Mukesh R, Analysis of big data security practices, Proceedings of the 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2017, pp. 264-267.

      [12] Shen P, Zhou Y, Chen K, A Probability based Subnet Selection Method for Hot Event Detection in Sina Weibo Microblogging, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013, pp. 1410-1413.

      [13] Chen K, Zhou Y, Zha H, He J, Shen P, Yang X, Cost-Effective Node Monitoring for Online Hot Event Detection in Sina Weibo, Proceedings of the 22nd international conference on World Wide Web, 2013, pp. 107-108.

      [14] Kempe D, Klenberg J, Tardos E, Maximizing the spread of influence through a social netowrk, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003, pp. 137-146.

      [15] Park S O, The Framework for Providing Compatibility to various Web Browser Plug-ins, Master's Thesis, KAIST, 2009.

      [16] Joelsson T, Mobile Web Browser Extensions, Master of Science Thesis, KTH Information and Communication Technology, 2008.

      [17] Miao B B, Jin X B, Compression processing estimation method for time series big data,Processing of the 27th Chinese Control and Decision Conference(2015 CCDC), 2015, pp. 1807-1811.

      [18] Barbieru C, Pop F, Soft Real-Time Hadoop Scheduler for Big Data Processing in Smart Cities, Proceedings of the 2016 IEEE 30th International Conference on Advanced Information Networking and Application (AINA), 2016, pp. 863-870.

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  • How to Cite

    Jeong, Y.-S., Kim, Y.-T., & Park, G.-C. (2018). Efficient Big Data-Based Access Control Mechanism for IoT Cloud Environments. International Journal of Engineering & Technology, 7(4.39), 671-676. https://doi.org/10.14419/ijet.v7i4.39.25679