While I think data synthesis is a useful technique I never understood how most synthetization approaches can so confidently claim that there is no leakage of se

While I think data synthesis is a useful technique I never understood how most synthetization approaches can so confidently claim that there is no leakage of sensitive information into the synthesis model. The only approach I've seen for this is using differential privacy techniques to limit the amount of information that can be learned by the synthesis model from a given datapoint, but doing so also drastically limits the ability of the model to learn from the input data. Another issue I have with data synthetization is that by trying to reproduce the original data as faithfully as possible you waste a lot of your privacy budget on meaningless features. That said I'm happy to be convinced otherwise. Do you have a publicly available demo dataset that I could look at?