Reverse engineer scikit-learn serialized model
No, you cant (in principle, anyway) reverse engineer the data based on a model. You can obviously derive the trained model weights/etc and start to get a good understanding of what it might have been trained over, but directly deriving the data, I'm not aware of any possible way of doing that, providing you're pickling the trained model.
I am trying to understand the security implications of serializing a scikit-learn/keras fitted model (using pickle/joblib etc). 
Specifically, if I work on data that I don't want to be revealed, would there be anyway for someone to reverse engineer what data a model was fitted on? Or is the data, just a way for the algorithm to update the relevant coefficients/weights for the algorithm? (If I train the model against "This movie is great" and store it as a foo.pkl file, would I also be able to load the foo.pkl and say it was trained on "This movie is great" if all I have access to is the pkl file and not the data)