You can make up theoretical models for how many popular libraries there are, or how many sites, and say this should or shouldn't be possible. I haven't seen any such models, but advertisers were definitely using this technique in the wild so the models that say it's impossible are all wrong.

Adding noise to a small sample of requests doesn't buy you that much entropy - the signal is a little noisy anyway.

Think about the 10,000s of JavaScript libraries out there, and the 100s of versions of each.

A good intuition is that although there's low certainty which site you have visited, there's high certainty which sites you HAVE NOT visited

I think is easier to see how you can fingerprint someone based on the set of sites they HAVE NOT visited

Edit: I'm not so sure anymore, I think that requires to test looots of libraries, it's not practical unless you are a really nasty ad company that tests hundreds of libraries in the background really sucking up your bandwidth

Imagine ad companies choose libraries that are roughly used by 50% of users, if they test you with 10 of those, they learn ~10bits of entropy to classify you, i.e. which of the 1024 classifications you belong