A Review on Privacy Preserving Data Mining Using Piecewise Vector Quantization

Abstract

Now in days, Privacy Preserving Data Mining (PPDM) is the most important area of the Data Mining,
which aims to protect the sensitive data comes from different businesses and organizations. Such data is very useful for
data owner to make decision based on the mining result. But the privacy of the data may prevent the data owners from
sharing their data for analysis purpose. The Privacy Preserving Data Mining has become increasingly popular because
it allows sharing sensitive data for analysis purposes. In this paper we present some key directions in the field of privacy
preserving data mining and various dimensions of privacy preserving techniques. We provide a review on piecewise
vector quantization approach which segmentized each row of dataset and quantization approach will performed on each
segment using K-means which later are united to form a transformed dataset.