Privacy Preserving Data Mining Algorithms by Data Distortion

Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting sensitive information simultaneously. In this paper, we present a generic PPDM framework and a classification scheme for centralized database, adopted from early studies, to guide the review process. Frequencies of different techniques/algorithms used are tableau and analyzed. A set of metrics and a theoretical framework are also proposed for assessing the relative performance of selected PPDM algorithms. Finally, we share directions for future research.