A Survey: Privacy Preserving Data Mining Techniques

Abstract

Data mining is used to retrieve relevant information from large datasets by using various data analysis tools in order to identify different patterns in data to make predictions. Many organizations like government sectors, business, education, medical and defense fields should protect data during mining process. Confidentiality, leakage and exposing of data should be considered and prevented by using appropriate  data mining techniques. Important factors like sharing and publishing of data should be considered during data mining process. Privacy preservation solves the problem of leakage and exposing of data  during data mining process and also provides secure and reliable information sharing between parties. In this paper, different privacy preservation data mining methods like anonymization, randomization, condensation, and cryptography were reviewed and compared. Experiments were performed using k-anonymity, l-diverse and  t-closeness techniques.