Data Mining Over Encrypted Data of Database Client Engine Using Hybrid Classification Approach

Bhagyashree Ambulkar

doi:10.21276/ijircst.2017.5.3.7

Abstract

Data mining has been used in various areas, for example crime agencies, retail industries, financial data analysis, telecommunication industry, biological, among government agencies, etc. Several application handle very delicate data. So these data remains secure and private. In data mining, Classification could be the one of the major task. Going back two full decades various privacy issues are occurs so that many conceptual and feasible solutions to the classification problem have been developed. Similarly daily cloud user is increment tremendously and they have a big possibility to process the offload the information an encrypted form. The information in the cloud has been in encrypted form, recent privacy preserving classification systems are not feasible. In this paper, our proposed hybrid method provides privacy -preserving classifier for encrypted data of relational database and also achieves the marginally better performance for extracting information using k-NN algorithm from encrypted data of relational databases. This paper describes AES encryption technique which is highly secure and efficient.

Keywords

AES Encryption Algorithm, kNN classification, Privacy Preserving Classification, RTree.

References

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Cites this article as

[Bhagyashree Ambulkar, Prof. Gunjan Agre (2017), Data Mining Over Encrypted Data of Database Client Engine Using Hybrid Classification Approach, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-5, Issue-3, Page No-291-294], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

Bhagyashree Ambulkar

M. Tech. Scholar, Department of Computer Science and Engineering, Nagpur Institute of Technology, Nagpur, Nagpur, Maharashtra, India, 9763316989.

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