Implementation of Deduplication on Encrypted Big-data using Signcryption for cloud storage applications

Saravanan, Palani, Sangeetha, E and Archana, A Implementation of Deduplication on Encrypted Big-data using Signcryption for cloud storage applications. International Journal of Pure and Applied Mathematics, 2018, vol. 119, n. 12, pp. 13409-13421. [Journal article (Paginated)]

[thumbnail of 1222.pdf]
Preview
 Text
1222.pdf
Download (510kB) | Preview

English abstract

As Big Data Cloud storage servers are getting widespread the shortage of disc space within the cloud becomes a major concern. The elimination of duplicate or redundant data, particularly in computer data is named deduplication. Data deduplication is a method to regulate the explosive growth of information within the cloud storage, most of the storage providers are finding more secure and efficient methods for their sensitive method. Recently, a noteworthy technique referred to as signcryption has been proposed, in which both the properties of signature (ownership) and encryption are simultaneously implemented with better performance According to deduplication, we introduce a method that can eliminate redundant encrypted data owned by different users. Furthermore, we generate a tag which will be the key component of big data management. We propose a technique called digital signature for ownership verification. Convergent encryption also called for a content hash key cryptosystem. Convergent encryption is an encryption approach that supports deduplication. With this encryption technique, the encryption key is generated out of a hash of plain text. Therefore applying this technique, identical plaintexts would turn out the same ciphertext.

Item type: Journal article (Paginated)
Keywords: Big data, digital signature, cloud, data deduplication, convergent encryption
Subjects: B. Information use and sociology of information
B. Information use and sociology of information > BC. Information in society.
Depositing user: Raster Daster
Date deposited: 02 Aug 2018 07:32
Last modified: 02 Aug 2018 07:32
URI: http://hdl.handle.net/10760/33262

References

[1]. C. Fan, S. Y. Huang, and. C. Hsu, Hybrid data deduplication in a cloud environment, in Proc. Int. Conf. Inf. Secure. Intell. Control, 2012, pp. 174177

[2]. Encrypted Data Deduplication in Cloud Storage, Information Security (AsiaJCIS), 2015 10th Asia Joint Conference on, Issue Date: 24-26 May 2015

[3]. M. Bellare, S. Keelveedhi, and T. Ristenpart, “DupLESS: Server aided encryption for deduplicated Storage,” in Proc. 22nd USENIX Conf. Secur., 2013, pp. 179–194.

[4]. Chuanyi Liu, Xiaojian Liu and Lei Wan “Policy-based deduplication in secure cloud storage,” in Proc. Trustworthy Compute. Serv., 2013, pp. 250–262, doi: 10.1007/978-3-642-35795-4_32.

[5]. Y.Saritha, A. Vineela, "Efficient Deduplication On encrypted Big Data in Cloud”, International Journal of emerging trends and technology in computer science, vol.6, no.4, 2017.

[6]. Zhang, D., Liao, C., Yan, W., Tao, R., & Zheng, W. (2017, August). Data Deduplication Based on Hadoop. In Advanced Cloud and Big Data (CBD), 2017 Fifth International Conference on (pp. 147-152). IEEE.

[7]. Hui Cui, Robert H. Deng, Yingjiu Li, and Guowei Wu “Attribute Based Storage supporting secure deduplication of encrypted data in cloud” IEEE Transactions on Big Data, January 2017

[8]. Sekar, K. R., Saravanan, P., & Sethuraman, J. (2006). Discovery of right binary pattern in key management using security matrix and magic cards. ARPN Journal of Engineering and Applied Sciences, 11(15), pp. 9187-9194

[9]. Jin Li, Yan Kit Li, Xiaofeng Chen, Patrick P.C. Lee, and Wenjing Lou “A Hybrid Cloud Approach for Secure Authorized Deduplication” IEEE Transactions On Parallel And Distributed Systems, Vol. 26, No. 5, May 2015

[10]. C. Fan, S. Y. Huang, and W. C. Hsu, “Hybrid data deduplication in cloud environment,” in Proc. Int. Conf. Inf. Secur. Intell. Control, 2012, pp. 174–177, doi:10.1109/ISIC.2012.6449734.

[11]. Nesrine Kaaniche, Maryline Laurent “A Secure Client Side Deduplication Scheme in Cloud Storage Environments” IEEE Transactions on Mobility and Security (NTMS) in Cloud Computing, Issue Date:April.2.2014

[12]. C. Yang, J. Ren, and J. F. Ma, “Provable ownership of file in deduplication cloud storage,” in Proc. IEEE Global Commun. Conf, 2013,pp.695700,doi:10.1109/ GLOCOM. 2013.6831153.

[13]. J. W. Yuan and S. C. Yu, “Secure and constant cost public cloud storage auditing with deduplication,” in Proc. IEEE Int. Conf. Communic. Netw. Secur., 2013, pp. 145–153, doi:10.1109/CNS.2013. 6682702.

[14]. T. Y. Wu, J. S. Pan, and C. F. Lin, “Improving accessing efficiency of cloud storage using de-duplication and feedback schemes,”IEEE Syst. J., vol. 8, no. 1, pp. 208–218, Mar. 2014, doi:10.1109/ JSYST.2013.2256715.

[15] Indragandhi, V., Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Siarry, P., & Uden, L. (2018). Multi-objective optimization and energy management in renewable based AC/DC microgrid. Computers & Electrical Engineering.

[16] Subramaniyaswamy, V., Manogaran, G., Logesh, R., Vijayakumar, V., Chilamkurti, N., Malathi, D., & Senthilselvan, N. (2018). An ontology-driven personalized food recommendation in IoT-based healthcare system. The Journal of Supercomputing, 1-33.

[17] Arunkumar, S., Subramaniyaswamy, V., & Logesh, R. (2018). Hybrid Transform based Adaptive Steganography Scheme using Support Vector Machine for Cloud Storage. Cluster Computing.

[18] Indragandhi, V., Subramaniyaswamy, V., & Logesh, R. (2017). Resources, configurations, and soft computing techniques for power management and control of PV/wind hybrid system. Renewable and Sustainable Energy Reviews, 69, 129-143.

[19] Ravi, L., & Vairavasundaram, S. (2016). A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Computational intelligence and neuroscience, 2016, Article ID: 1291358.

[20] Logesh, R., Subramaniyaswamy, V., Malathi, D., Senthilselvan, N., Sasikumar, A., & Saravanan, P. (2017). Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback. Biomedical Research, 28(13), 5646-5650.

[21] Arunkumar, S., Subramaniyaswamy, V., Karthikeyan, B., Saravanan, P., & Logesh, R. (2018). Meta-data based secret image sharing application for different sized biomedical images. Biomedical Research,29.

[22] Vairavasundaram, S., Varadharajan, V., Vairavasundaram, I., & Ravi, L. (2015). Data mining‐based tag recommendation system: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(3), 87-112.

[23] Logesh, R., Subramaniyaswamy, V., & Vijayakumar, V. (2018). A personalised travel recommender system utilising social network profile and accurate GPS data. Electronic Government, an International Journal, 14(1), 90-113.

[24] Vijayakumar, V., Subramaniyaswamy, V., Logesh, R., & Sivapathi, A. (2018). Effective Knowledge Based Recommeder System for Tailored Multiple Point of Interest Recommendation. International Journal of Web Portals.

[25] Subramaniyaswamy, V., Logesh, R., & Indragandhi, V. (2018). Intelligent sports commentary recommendation system for individual cricket players. International Journal of Advanced Intelligence Paradigms, 10(1-2), 103-117.

[26] Indragandhi, V., Subramaniyaswamy, V., & Logesh, R. (2017). Topological review and analysis of DC-DC boost converters. Journal of Engineering Science and Technology, 12 (6), 1541–1567.

[27] Saravanan, P., Arunkumar, S., Subramaniyaswamy, V., & Logesh, R. (2017). Enhanced web caching using bloom filter for local area networks. International Journal of Mechanical Engineering and Technology, 8(8), 211-217.

[28] Arunkumar, S., Subramaniyaswamy, V., Devika, R., & Logesh, R. (2017). Generating visually meaningful encrypted image using image splitting technique. International Journal of Mechanical Engineering and Technology, 8(8), 361–368.

[29] Subramaniyaswamy, V., Logesh, R., Chandrashekhar, M., Challa, A., & Vijayakumar, V. (2017). A personalised movie recommendation system based on collaborative filtering. International Journal of High Performance Computing and Networking, 10(1-2), 54-63.

[30] Senthilselvan, N., Udaya Sree, N., Medini, T., Subhakari Mounika, G., Subramaniyaswamy, V., Sivaramakrishnan, N., & Logesh, R. (2017). Keyword-aware recommender system based on user demographic attributes. International Journal of Mechanical Engineering and Technology, 8(8), 1466-1476.

[31] Subramaniyaswamy, V., Logesh, R., Vijayakumar, V., & Indragandhi, V. (2015). Automated Message Filtering System in Online Social Network. Procedia Computer Science, 50, 466-475.

[32] Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., & Indragandhi, V. (2015). Unstructured data analysis on big data using map reduce. Procedia Computer Science, 50, 456-465.

[33] Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., & Indragandhi, V. (2015). Intelligent travel recommendation system by mining attributes from community contributed photos. Procedia Computer Science, 50, 447-455.

[34] Vairavasundaram, S., & Logesh, R. (2017). Applying Semantic Relations for Automatic Topic Ontology Construction. Developments and Trends in Intelligent Technologies and Smart Systems, 48.

[35] Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X. Z., & Indragandhi, V. (2017). A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Generation Computer Systems, 83, 653-673.

[36] Subramaniyaswamy, V., & Logesh, R. (2017). Adaptive KNN based Recommender System through Mining of User Preferences. Wireless Personal Communications, 97(2), 2229-2247.

[37] Logesh, R., & Subramaniyaswamy, V. (2017). A Reliable Point of Interest Recommendation based on Trust Relevancy between Users. Wireless Personal Communications, 97(2), 2751-2780.

[38] Logesh, R., & Subramaniyaswamy, V. (2017). Learning Recency and Inferring Associations in Location Based Social Network for Emotion Induced Point-of-Interest Recommendation. Journal of Information Science & Engineering, 33(6), 1629–1647.

[39] Subramaniyaswamy, V., Logesh, R., Abejith, M., Umasankar, S., & Umamakeswari, A. (2017). Sentiment Analysis of Tweets for Estimating Criticality and Security of Events. Journal of Organizational and End User Computing (JOEUC), 29(4), 51-71.


Downloads

Downloads per month over past year

Actions (login required)

View Item View Item
Contact us
Send email Sponsors
Sponsor: AIMS Sponsor: D&P Sponsor: UniNa CAB Get connected
Facebook Facebook Twitter Twitter Linkedin LinkedIn RSS RSS