Privacy Preserving Data Mining: Survey of Approaches

Privacy is one of the most important properties of an information system must satisfy, in which systems the need to share information among different, not trusted entities, the protection of sensible information has a relevant role. Thus privacy is becoming an increasingly important issue in many data mining applications. For that privacy secure distributed computation, which was done as part of a larger body of research in the suppression, cryptography, randomization, sumarization has achieved remarkable results. These results were shown using generic constructions that can be applied to any function that has an efficient representation as a circuit. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy when data mining techniques are used in a malicious way. Privacy preserving data mining algorithms have been recently introduced with the aim of preventing the discovery of sensible information. In this paper we will describe the implementation of suppression, cryptography, randomization, sumarization in that data mining for privacy preserving.