I am thinking machine learning. I am thinking I don't want a developer to run machine learning algorithms in a (unsanitized) development environment with access to production data.

How do you train validate and market test an ml model without access to production data? Please teach us all, this is literally a billion dollar industry question.

You duplicate the data in dev but remove the names, SSNs, emails, addresses, phone numbers, etc?

That's not enough even, in previous cases we've seen researchers augment datasets with other data to de-anonymize. https://www.wired.com/2007/12/why-anonymous-data-sometimes-i...

But I think in general the idea OP was dismissing was generating synthetic data rather than attempting to anonymize prod data. In that case you have a risk of modeling the generator rather than your users.

PII is only one type of sensitive information, there is a lot more.

The bottom line that if you want to draw insights from data, that particular data must make its way to the engineer unadulterated. If you de-risk some fields in your data via removal/masking/entropy, then you are excluding those fields from ml.

You are not talking about blinded machine learning, you're talking about not doing machine learning on sensitive data in the first place. The whole discussion is moot.

If it's literally a billion dollar industry question, then hire me. I'm not interested in continuing to feed corporations for free.