Differential privacy noise calibration

privacy

Adding calibrated noise (e.g. Laplace/Gaussian mechanism) to protect individual privacy in aggregate counts can distort small-cell counts enough to invert rank ordering between nearby cells if the privacy budget (epsilon) is set too aggressively for the use case.

Detection

Compare noised counts against raw (internal-only) counts for rank-order stability across repeated noise draws at the chosen epsilon.

Mitigation

Tune epsilon per use case, tighter for public releases, looser for internal-only decision support with other controls
Report a confidence interval alongside any noised count so users don't over-read small differences

Affected conversions

Point → H3 exact
Raster → H3 approximate