PyDP VS differential-privacy-library

As the title mentioned, I studied some tutorials about differential privacy and the examples of PyDP, but they only deal with simple cases(structure text). Which paper/direction I should focus on if I want to make the unstructured medical data private? Is it possible to make the data private with some preprocessing before I feed the data into the model? A naive idea is find out the sensitive part(ex : name), change them to non sensitive text manually. Thanks

Differential privacy. Basically i wanted to create a randomly generated database file, akin to medical records, create a Private Aggregation of Teacher Ensembles algorithms based on 20-60% of its content and then use this teacher model on the other 80-40% of database which was just a plaintext, not that that matters. The problem is, I've barely got ideas on how it all works, and the one example I've found used Cryptonumeric's library called cn.protect. And that went like I've already described. I've fallen back on practical part of the paper and found another way of getting any practical usage as the assignment requires and now am trying to use https://github.com/IBM/differential-privacy-library and the example on 30s guide to instead make the practical part about choosing epsilon ( a measure of how much information you can give away as a result of one query on the database to a third malicious party) by tracking associated accuracy of result dataset compared to original. I hope I'll manage to edit the code to accept my text file after parsing it through into ndarray from txt, separating the last column to use as a target and going from there.

IBM's Diffprivlib is a well-documented implementation of differential privacy in Python. Source code and getting started documentation is available on the IBM differential-privacy-library Github repository.