Preserving Privacy of Sensitive Data using Anonymization Technique

Abstract

Today, data mining techniques play an important role in finding useful information from large amounts of data. The extracted data may contain some personal information about individuals. There is a high probability of hacking personal information. Therefore, protecting privacy becomes an important factor in data mining. Many privacy protection techniques have been developed to hide private information about people. An important process of protecting privacy is anonymization. Many anonymity methods are used to protect the privacy of individuals. However, it still has shortcomings in the personal protection of personal information. Therefore, a new approach for effective anonymization is proposed in this study. Here, the feature selection algorithm based on the main content analysis can be used to identify the negative features of the data.

In this algorithm, eigenvalues ​​and eigenvectors are estimated. Anonymization is then accomplished by introducing the Novel Approach for Anonymization. Finally get anonymous data that protect personal data from hacking. The success of privacy protection here can be determined by performance such as electronic data, privacy level and cost accounting. From the experimental analysis, the performance of the proposed system shows its superiority over other systems.