Enhanced Hybrid Privacy Preserving Data Mining Technique
Naga Prasanthi Kundeti, Chandra Sekhara Rao Mvp, Sudha Sree Chekuri, Seshu Babu Pallapothu · IntechOpen eBooks · 2022
At present, almost every domain is handling large volumes of data even as storage device capacities increase. Amidst humongous data volumes, Data mining applications help find useful patterns that can be used to drive business growth, improved services, better health care facilities etc. The accumulated data can be exploted for identity theft, fake credit/debit card transactions, etc. In such scenarios, data mining techniques that provide privacy are helpful. Though privacy-preserving data mining techniques like randomization, perturbation, anonymization etc., provide privacy, but when applied separately, they fail to be effective. Hence, this chapter suggests an Enhanced Hybrid Privacy Preserving Data Mining (EHPPDM) technique by combining them. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy as well as evidenced by our experimental results.