Preserving Privacy through Data Generation

Abstract

Many databases will not or can not be disclosed without strong guarantees that no sensitive information can be extracted. To address this concern several data perturbation techniques have been proposed. However, it has been shown that either sensitive information can still be extracted from the perturbed data with little prior knowledge, or that many patterns are lost. In this paper we show that generating new data is an inherently safer alternative. We present a data generator based on the models obtained by the MDL-based KRIMP (Siebes et al., 2006) algorithm. These are accurate representations of the data distributions and can thus be used to generate data with the same characteristics as the original data. Experimental results show a very large pattern-similarity between the generated and the original data, ensuring that viable conclusions can be drawn from the anonymised data. Furthermore, anonymity is guaranteed for suited databases and the quality-privacy trade-off can be balanced explicitly.