Abstract
Although there are a number of anonymization techniques in the microdata publication, two problems remain: (1) the privacy breaches with auxiliary knowledge; (2) the large information losses during the anonymization. We establish the requirement of presence anonymity and propose the two-step process of synthesizing, consisting of learning a model from the original data, and then sampling a published version with it, which has the similar statistical characteristics and includes fake records. The advantage is that it prevents the auxiliary knowledge attacks as well as enables researchers get correct or approximately correct conclusions. Furthermore, its effectiveness is proved through extensive experiments.
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Gu, J., Chen, Y., Fu, J., Peng, H., Ye, X. (2010). Synthesizing: Art of Anonymization. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15364-8_33
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DOI: https://doi.org/10.1007/978-3-642-15364-8_33
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