Survey on Hybrid Anonymization using k-anonymity for Privacy Preserving in Data Mining

Abstract

K-anonymity is the one of the popular privacy preserving model. In the data mining there is multiple technique is available k-anonymity is one of the technique which is used for the protecting privacy in the database. In this paper our main approach is hybrid anonymization. The main thing of this technique is that it is the mixing of two techniques. We introduce hybrid anonymization with hybrid generalization which is formed by not only generalization but also the data relocation. Data relocation serves trade-off between truthfulness and utility. Using the hybrid anonymization we maintain the privacy standard such as k-anonymity. In the previous research we find that k-anonymity is not good work with multiple sensitive data and there is more information loss occurs for that issue we use hybrid anonymization on multiple dataset. We show that our model can decrease the information loss in minimum time period.