A Literature analysis on Privacy Preserving Data Mining
Privacy Preserving Data Mining (PPDM) is a research area concerned with the privacy driven from personally identifiable information when considered for data mining. Therefore, PPDM has become an increasingly important field of research. PPDM is a novel research direction in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provides a complete review on PPDM and different techniques such as data partition, data modification, data restriction technique which could be used to prevent the data access from unauthorized users. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy Sensitive data for analysis purposes. Several data mining algorithms, incorporating privacy preserving mechanisms, have been developed that allow one to extract relevant knowledge from large amount of data, while hide sensitive data or information from disclosure or inference. We provide a review of the state-of-the-art methods for privacy and analyze the representative technique for privacy preserving data mining.