Two Noise Addition Methods For Privacy-Preserving Data Mining

Abstract

In the last decade, more and more researches have focused on privacy-preserving data min ing(PPDM). The previous work can be div ided into two categories: data modification and data encryption. Data encryption is not used as widely as data modificat ion because of its high cost on computing and communications. Data perturbation, including additive noise, mu lt iplicative noise, matrix mu ltip lication, data swapping, data shuffling, k-anonymization, Blocking, is an important technology in data modification method. PPDM has two targets: privacy and accuracy, and they are often at odds with each other. Th is paper begins with a proposal of two new noise addition methods for perturbing the original data, followed by a discussion of how they meet the two targets. Experiments show that the methods given in this paper have higher accuracy than existing ones under the same condition of privacy strength.