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Enhanced Hybrid Privacy Preserving Data Mining Technique


Kundeti Naga Prasanthi, M V P Chandra Sekhara Rao, Ch Sudha Sree, P Seshu Babu


Vol. 23  No. 6  pp. 99-106


Now a days, large volumes of data is accumulating in every field due to increase in capacity of storage devices. These large volumes of data can be applied with data mining for finding useful patterns which can be used for business growth, improving services, improving health conditions etc. Data from different sources can be combined before applying data mining. The data thus gathered can be misused for identity theft, fake credit/debit card transactions, etc. To overcome this, data mining techniques which provide privacy are required. There are several privacy preserving data mining techniques available in literature like randomization, perturbation, anonymization etc. This paper proposes an Enhanced Hybrid Privacy Preserving Data Mining(EHPPDM) technique. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy. The experimental results show that classification accuracies have increased using EHPPDM technique.


privacy, privacy preserving data mining, k-anonymization, perturbation, l-diversity.