Privacy Preservation by Anonymization Method Accomplishing Concept of Hierarchical Clustering and DES: A Propose Study
Jeetendra Mittal, Akash Saxena · 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC) · 2017
Data mining has been substantially studied and useful into numerous fields which include the Internet of Things (IoT) and the business growth. However, data mining approaches also take place serious challenges due to enlarged sensitive information disclosure and the violation of privacy. Privacy-Preserving Data Mining also called (PPDM), as an essential branch of the data mining and an exciting topic in privacy preservation, has gain particular attention in current years. This discussion describes the privacy concern that occurs due to data mining, particularly for the national security applications. We discuss privacy-preserving data mining by Anonymization Method in which we use hierarchical clustering in order to divide the given data and DES algorithm for encryption of data in order to prevent sensitive data from attacker.