Privacy Preserving Data Mining Based On Perturbation Approach: A Survey

Data mining is the technique for extracting useful information from the database. It has been an increasing important tool for transforming data into information. Data mining techniques has been developed successfully to extracts knowledge in order to support a variety of domains marketing, weather forecasting medical diagnosis, and national security. But it is still challenge to mine certain kinds of data without violating the data owner’s privacy. Privacy preserving is developed as an important concern with reference to the success of the data mining. Privacy preserving data mining deals with protecting the privacy of individual data or sensitive knowledge without sacrificing the utility of the data. People have become well aware of the privacy intrusions on their personal data and are very reluctant to share their sensitive information. In recent years, the wide availability of personal data has made the problem of privacy preserving data mining an important one. A number of methods have recently been proposed for privacy preserving data mining. In this paper define on the various Privacy Preserving Data mining (PPDM) Techniques are available. But the mainly focus Data perturbation technique is a popular technique in privacy-preserving data mining. A major challenge in data perturbation is to balance privacy protection and data utility, which are normally considered as a pair of conflicting factors. We argue that selectively preserving the task/model specific information in perturbation will help achieve better privacy guarantee and better data utility.