A new algorithm-independent method for privacy-preserving data mining

With the development of data mining technologies, privacy protection is becoming a challenge for data mining applications in many fields. To solve this problem, many PPDM (privacy-preserving data mining) methods have been proposed. One important type of PPDM method is based on data perturbation. Only part of the data-perturbation-based methods is algorithm-irrelevant, which are favorable because common data mining algorithms can be used directly. This paper proposes a new algorithm-irrelevant PPDM method based on data perturbation. Our method generates a new data set that is different from and has the same distribution as the original data set. Our experiments show that this new method can produce usable data while protecting privacy well. © 2014 Binary Information Press.