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 reduce multi-view violation detection problem to the single view case. Finally, we adopt the publicly available data set, Adult Database, at the UC Irvine Machine Learning Repository, and 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.