Differential privacy techniques inject structured noise into data streams.

That makes any given observation swamped by the noise and thus not identifying, but allows the overall true shape of the distribution to be visible at scale.

This exploits the "consistent bias at enough scale can emerge even from noisy streams" phenomena that powers all emergent phenomena–including evolutionary gradients.