Usage
then_private_quantile(lhs, output_measure, candidates, alpha, scale)
Arguments
- lhs
The prior transformation or metric space.
- output_measure
Either MaxDivergence or ZeroConcentratedDivergence.
- candidates
Potential quantiles to score
- alpha
a value in \([0, 1]\). Choose 0.5 for median
- scale
the scale of the noise added