A Novel Approach in Cloud for Secure Authorized De- Duplication

International Journal of Computer Science and Engineering
© 2015 by SSRG - IJCSE Journal
Volume 2 Issue 7
Year of Publication : 2015
Authors : Putta Kalyan Kumar, B.Venkaiah Chowdary

pdf
How to Cite?

Putta Kalyan Kumar, B.Venkaiah Chowdary, "A Novel Approach in Cloud for Secure Authorized De- Duplication," SSRG International Journal of Computer Science and Engineering , vol. 2,  no. 7, pp. 27-30, 2015. Crossref, https://doi.org/10.14445/23488387/IJCSE-V2I7P109

Abstract:

Data deduplication is the mechanism using which the data storage process can be improved. It is one of the compressions like technique where by using this mechanism we can limit the storage and efficiently utilize the database storage capacity. Deduplication is the process of eliminating the similar kind of data from the storage device to reduce the amount of storage space and save bandwidth. For the security reasons, to save data stored at cloud we need to encrypt the data and provide security key with which the data can be used by the end user. Deduplication can be achieved in three ways file level, content level and byte level. In this work we would like to work on file level deduplication, content level deduplication and around 50% of byte level deduplication. The complete mechanism of deduplication will be carried out in the cloud architecture.

Keywords:

Deduplication, Compressions, Encryption, Cloud Architecture.

References:

[1] R. Agrawal, T. Imielinski, and A. Swami, “Mining AssociationRules between Sets of Items in Large Databases,” ACM SIGMODRecord, vol. 22, pp. 207-216, 1993. 
[2] R. Agrawal and G. Psaila, “Active Data Mining,” Proc.First Int‟lConf. Knowledge Discovery and Data Mining, pp. 3-8, 1995. 
[3] R. Agrawal and R. Srikant, “Mining Generalized AssociationRules,” Proc. 21th Int‟l Conf. Very Large Data Bases (VLDB ‟95),pp. 407-419, 1995. 
[4] M.L. Antonie, O.R. Zaiane, and A. Coman, “Application of DataMining Techniques for Medical Image Classification,” Proc.SecondInt‟l Workshop Multimedia Data Mining (MDM/KDD ‟01), 2001. 
[5] W.-H. Au and K.C.C. Chan, “Mining Changes in AssociationRules: A Fuzzy Approach,” Fuzzy Sets Systems, vol. 149, pp. 87-104, Jan. 2005. 
[6] E. Baralis, L. Cagliero, T. Cerquitelli, V. D‟Elia, and P. Garza,“Support Driven Opportunistic Aggregation for GeneralizedItemsetExtraction,” Proc. IEEE Fifth Int‟l Conf. IntelligentSystems (IS ‟10), 2010. 
[7] S. Baron, M. Spiliopoulou, and O. Gnther, “Efficient Monitoring ofPatterns in Data Mining Environments,” Advances in Databases andInformation Systems, L. Kalinichenko, R. Manthey, B. Thalheim,and U. Wloka, eds., vol. 2798, pp. 253