Privacy Preservation Approach using K-Anonymity Chinese Remainder Theorem for Intrusion Detection
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International Journal of Computer & Organization Trends (IJCOT) | 
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| © 2014 by IJCOT Journal | ||
| Volume - 4 Issue - 5 |
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| Year of Publication : 2014 | ||
| Authors : Sanjeevaiah Kuraganti , Jeevana Jyothi. P | ||
| DOI : 10.14445/22492593/IJCOT-V12P308 |
Citation
Sanjeevaiah Kuraganti , Jeevana Jyothi. P. "Privacy Preservation Approach using K-Anonymity Chinese Remainder Theorem for Intrusion Detection", International Journal of Computer & organization Trends (IJCOT), V4(5):31-38 Sep - Oct 2014, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Abstract
Privacy preservation is vital for machine learning and data mining, but measures created to protect financial information sometimes bring about a trade off: reduced utility of the workout samples. This work introduces a privacy preserving approach that could be put on decision-tree learning, without decrease in accuracy. It describes a procedure for the preservation of privacy of collected data samples if information of one`s sample database continues to be partially lost. Existing approach will not work well for sample datasets with low frequency, or if low variance within the distribution of every samples. This procedure converts the first sample data sets towards a category of unreal data sets, which actually the unique samples couldn`t be reconstructed with no entire team of unreal data sets. Existing approach doesn’t provide privacy toward the selected attributes resulting from miss classification error, also existing attribute selection measures doesn’t give optimal selection gain values. Proposed system will gives optimal attribute selection measures using improved c45 algorithm in addition to privacy on attributes. In this particular proposed implementation a fresh filtering technique for preprocessing the network attacks and an improved algorithm when it comes to the classification of KDDCUP 99 dataset. Proposed decision tree algorithm is undoubtedly an optimization method utilized for fine-tuning of a given features whereas random forest, a highly accurate classifier, is created here for various kinds of attacks classification. Proposed work will concentrate more on true positive rate compare to existing approaches. The approach works with other privacy preserving approaches, for instance cryptography, for really protection. Proposed work will concentrate more on true positive rate compare to existing approaches.
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Keywords
NID, PRIVACY RESERVING.