Abstract
We present a sound data-value-dependent method of detecting privacy violations in the context of multiple views publishing. We assume that privacy violation takes the form of linkages, that is, identifier-privacy value pair appearing in the same data record. At first, we perform a theoretical study of the following security problem: given a set of views to be published, if linking of two views does not violate privacy, how about three or more of them? And how many potential leaking channels are there? Then we propose a pre-processing algorithm of views which can turn multi-view violation detection problem into the single view case. Next, we build a benchmark with publicly available data set, Adult Database, at the UC Irvine Machine Learning Repository, and identity data set generated using a coherent database generator called Fake Name Generator on the internet. Finally, we conduct some experiments via Cayuga complex event processing system, the results demonstrate that our approach is practical, and well-suited to efficient privacy-violation detection.
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Dou, D., Coulondre, S. (2012). Detecting Privacy Violations in Multiple Views Publishing. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32597-7_46
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DOI: https://doi.org/10.1007/978-3-642-32597-7_46
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