engleski

Partially Synthetic Dataset Generated for the Testing Purposes on the Basis of Available Public Use Anonymized Microdata

Governments and organizations increasingly recognize huge opportunities in sharing and distribution of collected data, and research community must provide methods and algorithms for privacy preserving data publishing. Without access to the original microdata it is impossible to estimate the quality of developed anonymization methods or to compare the classification accuracy and the computational time of various algorithms applied both on anonymized and original datasets. We propose another high-quality microdata source for testing purposes - partially synthetic dataset generated on the basis of actual public use anonymized microdata set. The original distribution of the data should be simulated in a significant extent, as well as attribute value correlations or functional dependencies. Since the synthesized data are based on published microdata sets, it is expected that hidden complex patterns within a dataset can be also preserved.

Synthetic data; Confidentiality; Disclosure; Microdata; PPDP

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