Classification and Evaluation of Privacy Preserving Data Mining Methods
Negar Nasiri, Mohammad Reza Keyvanpour · 2020
In the recently age, the volume of information is growing exponentially. This data can be used in several fields such as business, healthcare, cyber security, etc. Extracting useful knowledge from raw information is an important process. But the challenge in this process is the sensitivity of this information, which has made owners unwilling to share sensitive information. This has led the study of the privacy of data in data mining to be a hot topic today. In our paper, an aim is made to prepare 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. Our main purpose of presenting this framework is 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.