> DRM for datasets isn't really a thing, since to explore, visualize, and train on them, you need access to the raw data, and then instead of your machine-learning function you can just pass the identity function to get the raw data.

Functional Encryption[1] is aiming at this exact problem. It's still really early stage, and it currently has a lot of caveats (the current algorithms are really expensive and only able to work with linear function AFAIK), but the field is moving rapidly, and we don't know how far it will get. There's also Fully Homomorphic Encryption [2], which is way cheaper and more versatile, but in that case, only the owner of the data can get the result of the calculation.

None of this is currently used at scale, but their is a lot of research on this field and some PoC are being built at Microsoft and IBM IIRC. There's also Cosmian[3], a French startup working on this topic. (Full disclosure, I know the founders)

[1]: https://en.m.wikipedia.org/wiki/Functional_encryption

[2]: https://en.m.wikipedia.org/wiki/Homomorphic_encryption#Fully...

[3]: https://cosmian.com/