Information concealment and redemption through data anonymization technique

Data mining enables us to ascertain information we do not anticipate to find in databases. There has been accumulative interest in the problem of building accurate data mining models over comprehensive data while protecting privacy at the level of individual records. A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, the present study addresses to the existing Educational Data Mining where a novel data safety procedure to maintain and conserve anonymized database based on Privacy Preserving Data Mining (PPDM) implemented through ARX - Data Anonymization tool and Waikato Environment for Knowledge Analysis (WEKA) tool is proposed. On verification, the proposed Privacy Preserving using K-anonymity (PPK-a) model ensures privacy preserving the sensitive information in the database which predicts only the needed data during the data extraction process. The main objectives of the present study are to provide privacy for the sensitive data in the database and to maintain the anonymized database forever in the real world context for the users in order to avoid any misuse.