Techniques like differential privacy do not work for some types of data models, including many of the more interesting/risky ones. I've never seen a technique that can deliver an analytical model at scale that is both analytically effective and anonymous while also robust against sophisticated de-anonymization attacks. There is no good theoretical foundation to suggest that such things are possible.
Most modern techniques for ensuring anonymization make assumptions that won't constrain sophisticated blackhats. They are good policy in a legal ass-covering sense and increase the cost required to de-anonymize but that is about it.
If your scale is large enough and you don’t care about identifying individuals, synthetic data does this fairly effectively.
This presents a small annoyance when I need to use another computer and start TYPING IN CAPS when I don’t intend to
This home row mod idea makes a lot of sense to me but I don’t think I would want to train my muscle memory to use such a drastic difference in behavior lest I find myself embarrassingly useless when presenting on a random work computer that doesn’t support this
The kbd community has all kinds of small form factor / isometric layouts like this that are totally custom and efficient but just make me think how you just train your hands to be shockingly unproductive on 99.99% of other systems
I’m proficient enough that I can work on anyone else’s machine well, and I have the comfort and speed benefits on my main workstation.