ENHANCING APPROACH USING HYBRID PAILLER AND RSA FOR INFORMATION SECURITY IN BIGDATA
Shadan Mohammed Jihad ABDALWAHID
shadanbaban@yahoo.comErbil Polytechnic University, Erbil Technical Engineering College, Department of Information System Engineering, Erbil (Iraq)
Raghad Zuhair YOUSIF
Erbil Polytechnic University, Erbil Technical Engineering College, Department of Information System Engineering, Erbil (Iraq)
Shahab Wahhab KAREEM
* Department of Applied Physics Communication, College of Science, Salahaddin University, Erbil, Kurdistan Region (Iraq)
Abstract
The amount of data processed and stored in the cloud is growing dramatically. The traditional storage devices at both hardware and software levels cannot meet the requirement of the cloud. This fact motivates the need for a plat¬form which can handle this problem. Hadoop is a deployed platform proposed to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data of the cloud system. Hadoop has no strategy to assure the safety and confidentiality of the files saved inside the Hadoop distributed File system (HDFS). In the cloud, the protection of sensitive data is a critical issue in which data encryption schemes plays avital rule. This research proposes a hybrid system between two well-known asymmetric key cryptosystems (RSA, and Paillier) to encrypt the files stored in HDFS. Thus before saving data in HDFS, the proposed cryptosystem is utilized for encrypting the data. Each user of the cloud might upload files in two ways, non-safe or secure. The hybrid system shows higher computational complexity and less latency in comparison to the RSA cryptosystem alone.
Keywords:
BigData, Hadoop, RSA, Paillier, CryptographyReferences
Bhardwaj, A., Singh, V. K., Vanraj, & Narayan, Y. (2015). Analyzing BigData with Hadoop Cluster in HDInsight Azure Cloud. Annual IEEE India Conference (INDICON). India: IEEE. https://doi.org/10.1109/INDICON.2015.7443472
DOI: https://doi.org/10.1109/INDICON.2015.7443472
Google Scholar
Ahamad, D., Akhtar, M., & Hameed, S. A. (2019). A Review and Analysis of Big Data and MapReduce. International Journal of Advanced Trends in Computer Science and Engineering, 8(1), 1–3.
DOI: https://doi.org/10.30534/ijatcse/2019/01812019
Google Scholar
Amrulla, G., Mourya, M., Sanikommu, R. R., & Afroz, A. A. (2018). A Survey of : Securing Cloud Data under Key Exposure. International Journal of Advanced Trends in Computer Science and Engineering, 7(3), 30–33.
DOI: https://doi.org/10.30534/ijatcse/2018/01732018
Google Scholar
Bhandarkar, M. (2010). MapReduce programming with apache Hadoop. International Symposium on Parallel & Distributed Processing (IPDPS) (pp. 1-2). Atlanta: IEEE .
DOI: https://doi.org/10.1109/IPDPS.2010.5470377
Google Scholar
Chandra, S., Alam, S. S., Paira, S., & Sanyal, G. (2014). A comparative survey of symmetric and asymmetric key cryptography. International Conference on Electronics, Communication and Computational Engineering (ICECCE) (pp. 83–93). IEEE.
DOI: https://doi.org/10.1109/ICECCE.2014.7086640
Google Scholar
Chandra, S., Bhattacharyya, S., Paira, S., & Alam, S. S. (2014). A Study and Analysis on Symmetric Cryptography. ICSEMR (pp. 1–8). IEEE.
DOI: https://doi.org/10.1109/ICSEMR.2014.7043664
Google Scholar
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. 6th Symposium on Operating Systems Design and Implementation (pp. 107–113). ACM.
DOI: https://doi.org/10.1145/1327452.1327492
Google Scholar
Dubey, A. K., Jain, V., & Mittal, A. P. (2015). Stock Market Prediction using Hadoop Map-Reduce Ecosystem. 2nd International Conference on Computing for Sustainable Global Development (pp. 616–621). IEEE.
Google Scholar
Hilbert, M. (2016). Big Data for Development: A Review of Promises and Challenges. Development Policy Review, 34(1), 135–174.
DOI: https://doi.org/10.1111/dpr.12142
Google Scholar
Inukollu, V. N., Arsi, S., & Ravuri, S. R. (2014). Security Issues Associted With big Data In Cloud Computing. International Journal of Network Security & Its Applications (IJNSA), 6(3), 45–56.
DOI: https://doi.org/10.5121/ijnsa.2014.6304
Google Scholar
Ismael, R. S., Youail, R. S., & Kareem, S. W. (2014). Image Encryption by Using RC4 Algorithm. European Academic Research, II(4), 5833–5839.
Google Scholar
Jam, M. R., Khanli, L. M., Akbari, M. K., & Javan, M. S. (2014 ). A Survey on Security of Hadoop. 4th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 716–721). IEEE.
DOI: https://doi.org/10.1109/ICCKE.2014.6993455
Google Scholar
Kareem, S. W. (2009). Hybrid Public Key Encryption Algorithms For E-Commerce. Erbil: University of Salahaddin–Hawler.
Google Scholar
Kareem, S. W., & Hussein, Y. T. (2017). Survey and New Security methodology of Routing Protocol in AD-Hoc Network. The 1st International Conference on Information Technology (pp. 452–464). Erbil.
DOI: https://doi.org/10.25212/ICoIT17.043
Google Scholar
Li, B., Wang, M., Zhao, Y., Pu, G., Zhu, H., & Song, F. (2015). Modeling and Verifying Google File System Modeling and Verifying Google File System. 16th International Symposium on High Assurance Systems Engineering (pp. 207–214). IEEE.
DOI: https://doi.org/10.1109/HASE.2015.38
Google Scholar
Mahmoud, H., Hegazy, A., & Khafagy, M. H. (2018). An approach for Big Data Security based on Hadoop Distributed File system. International Conference on Innovative Trends in Computer Engineering (ITCE 2018). Aswan: Aswan University.
DOI: https://doi.org/10.1109/ITCE.2018.8316608
Google Scholar
Merla, P., & Liang, Y. (2017). Data analysis using hadoop MapReduce environment. IEEE International Conference on Big Data (Big Data) (pp. 4783–4785). Boston: IEEE.
DOI: https://doi.org/10.1109/BigData.2017.8258541
Google Scholar
Motoyama, M., McCoy, D., Levchenko, K., Savage, S., & Voelker, G. M. (2011). An analysis of underground forums. ACM SIG- COMM Conference on Internet Measurement Conference IMC '11 (pp. 71–80). New York: ACM.
DOI: https://doi.org/10.1145/2068816.2068824
Google Scholar
Park, S., & Lee, Y. (2013). Secure Hadoop with Encrypted HDFS. International Conference on Grid and Pervasive Computing (pp. 134–141). Springer.
DOI: https://doi.org/10.1007/978-3-642-38027-3_14
Google Scholar
Parmar, R. R., Roy, S., Bhattacharyya, D., Bandyopadhyay, S. K., & Kim, T.-H. (2017). Large-scale encryption in the Hadoop environment: Challenges and solutions. IEEE Access, 5, 7156–7163.
DOI: https://doi.org/10.1109/ACCESS.2017.2700228
Google Scholar
Raghad, Z. Y., Kareem, S. W., & Hasan, A. O. (2016). Design Security System Based on AES and MD5 for Smart Card. Sulaimanyia: Charmo university.
Google Scholar
Shetty, M. M., & Manjaiah, D. H. (2016). Data security in Hadoop distributed file system. IEEE Int. Conf. Emerg. Technol. Trends Comput. Commun. Electr. Eng. ICETT 2016 (pp. 939–944). IEEE.
DOI: https://doi.org/10.1109/ICETT.2016.7873697
Google Scholar
Usama, M., & Zakaria, N. (2017). Chaos-Based Simultaneous Compression and Encryption for Hadoop. PLoS One.
DOI: https://doi.org/10.1371/journal.pone.0168207
Google Scholar
Yang, Ch., Lin, W., & Liu, M. (2013). A Novel Triple Encryption Scheme for Hadoop-based Cloud Data Security. Fourth International Conference on Emerging Intelligent Data and Web Technologies (pp. 437–442). IEEE.
DOI: https://doi.org/10.1109/EIDWT.2013.80
Google Scholar
Zhou, H., & Wen, Q. (2014). A new solution of data security accessing for Hadoop based on CP-ABE. 5th International Conference on Software Engineering and Service Science (pp. 525–528). IEEE.
DOI: https://doi.org/10.1109/ICSESS.2014.6933621
Google Scholar
Authors
Shadan Mohammed Jihad ABDALWAHIDshadanbaban@yahoo.com
Erbil Polytechnic University, Erbil Technical Engineering College, Department of Information System Engineering, Erbil Iraq
Authors
Raghad Zuhair YOUSIFErbil Polytechnic University, Erbil Technical Engineering College, Department of Information System Engineering, Erbil Iraq
Authors
Shahab Wahhab KAREEM* Department of Applied Physics Communication, College of Science, Salahaddin University, Erbil, Kurdistan Region Iraq
Statistics
Abstract views: 108PDF downloads: 19
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
