Classification and Evaluation of Privacy Preserving Data Mining Methods

In the last decades a huge number of information is produced per hour. This collected data can be used in some different fields such as business, healthcare, cybersecurity, after some process etc. in step two, the important process is that when this data is gathered, extraction of useful knowledge should be done from raw information. But the challenge that we face within this process, is the sensitivity of this information, which has made owners reluctant to share their sensitive information. This has led the study of the privacy of data in data mining to be a hot topic today. In this paper, an attempt is made to provide a framework for qualitative analysis of methods. This qualitative framework consists of three main sections: a comprehensive classification of proposed methods, proposed evaluation criteria, and their qualitative evaluation. In this case, we have a most important purpose of presenting this framework:1) systematic introduction of the most important methods of privacy-preserving in data mining 2) creating a suitable platform for qualitative comparison of these methods 3) providing the possibility of selecting methods appropriate to the needs of application areas 4) systematic introduction of points Weakness of existing methods as a prerequisite for improving methods of PPDM.