Abstract

Data mining technology that reveals knowledge in large databases could compromise the information that individuals or parties regard as private (sensitive). The aim is to find the right balance between maximizing analysis results (that are useful for the common good) and keeping the inferences that disclose sensitive information about parties or individuals at a minimum. This field is called privacy preserving data mining. Many algorithms and techniques have been proposed to solve this problem but the challenge is still open for new algorithms better algorithms and even better understanding and standardization for the problem of privacy preserving data mining. This paper presents a survey of privacy preserving in data mining tasks.