This is a minor, pedantic point, but what you really mean to say is "the closer the changes get to 50%, the more privacy the user has". If you change all results, then it is easy to flip them all back.
This distinction trips up several folks, where the research world initially believed (and some in official stats still believe[1]) that a part of privacy is literally not publishing the true answer (e.g. above: literally flip every output).
What you actually want is
Pr[output | input] ~= Pr[output | input']
which may mean that you should leave things alone, which feels weird but is important.[1]: Noise addition is a common way to obscure real-valued data, and some official stats bureaus have the ridiculous rule that "you always add noise, and you never add less than X in absolute value", leading to releases where you can be 100% confident that the true value is not in a range around the published number.
But still, there are some questions that you'd arguably never want to say "yes" to. Such as, did you visit some verboten site (terrorist, child porn, etc) today?
So how can an algorithm "know" which questions it's safe to use differential privacy with, and which it isn't?
Or would you argue that it's safe enough to use differential privacy with even such questions?
Or, in other words, the question is not "Did you visit this site," the question is "Did your coin come up tails or did you visit this site," which is a perfectly safe question to say "yes" to.
It may help with categories of sites, like "did you visit a news site today" or "did you watch porn".
For people who don't really need to care about privacy, I guess that differential privacy is good enough. But there's a gotcha there, for people who ought to care, but are clueless. I'm reminded of that ex cop in Philadelphia, who believed Freenet's claims about plausible deniability.
Edit: A prudent standard for calling something "[foo] privacy" is arguably PGP.