@b_cavello Okay, I've now skimmed the Leaking in Data Mining paper and watched Octavio Good's talk. They were both interesting and I learned things, but I'm not yet seeing how either one is related to either deidentification or differential privacy. Could you explain more?
At this point I'm nervous about any deidentification technique that doesn't have a differential privacy proof. There have been too many successful reidentification attacks; this feels like "don't roll your own crypto" again.
@jamey I don't think they're directly linked, exactly, but related in goal. The idea of training systems to ignore particular data to me seems hopeful for developing less biased models.
@b_cavello I still don't see the relation, but I agree that the use of adversarial networks to limit over-training was a really interesting part of that talk. I've seen stuff before about trying to remove bias from word2vec embeddings so that for example "doctor" doesn't get associated to "man" and "nurse" doesn't get associated to "woman", and I could imagine using the GAN approach to try to tackle that kind of problem too.
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