> The reality is, you can guarantee certain values are not passed. That’s fairly easy Nonsense. There's a fundamental requirement that for a model to have util

> The reality is, you can guarantee certain values are not passed. That’s fairly easy Nonsense. There's a fundamental requirement that for a model to have utility, it must have access to relevant data. If you truly find a leak-proof way to block certain values (which is borderline impossible in itself), then you make the model significantly weaker or outright useless if those values aren't uniformly distributed across the input domain. And if you do have leakage (and you almost certainly do), then direct re-identification becomes a trivial problem and we're back to square one. Synthetic data generated from statistical analysis of real data is either worthless or leaky. One of those two conditions is formally guaranteed to be true. GP is correct that a viable synthetic data generator basically has to have already solved your problem for you, and in that sense it just becomes your model which is trained on real data. Training an additional model on top of that model doesn't add any privacy or mitigate reidentification.