Instead, we have very strict privacy rules and experts to review the designs for the use of this data. If I even want to train a ML model over real data I have to have an approved privacy review that shows how you maintain privacy.
Where I use differential privacy algorithms in my line of work is to do ad-hoc analysis over suggestions placed in front of users. I have dimensions to aggregate across, but I want to ensure that no one bucket can deanonymize a user. k-anonymity used to be the thing (e.g. if a bucket has <50 people in it, that's too few), but even a large bucket can deanonymize users which is where k-anonymity comes in. I sincerely don't care who the users are, I just want to know how our features get used to try and save them more time.
Do I have access to the underlying logs? Yes. Can I use that to make decisions? No. I can however use the anonymized data to make decisions, and even store that longer than the underlying data exists (most logs exist for <14d).
Differential privacy also makes it possible to train models like SmartCompose by ensuring that the tokens it trains over are diffuse enough to not point back to any one person.
> I'd personally bet that differential privacy techniques that actually give users notable information-theoretic anonymity are very rarely used by Google in general.
For existing things, sure. They did their best, but this is new, reified research. As they're replaced they're being replaced by features which use differential privacy techniques.