This is all dependent on size of data sets & whether both train and test are equally representative of the domain you are trying to model. If you have thousands of data points and the test set is fully representative of the training set (hard to prove) then either method will be fine. If using a small but representative test data set then normalizing using the training parameters only is best as sampling errors may negatively bias the predictions. If the test is not very representative of the training set then you are comparing apples with oranges and should think again about your sampling procedure.