2 papers
cs.CR2025
Piquant: Private Quantile Estimation in the Two-Server Model
Hannah Keller, Jacob Imola, Fabrizio Boninsegna +2
Quantiles are key in distributed analytics, but computing them over sensitive data risks privacy. Local differential privacy (LDP) offers strong protection but lower accuracy than…
cs.DS2025
Differentially Private Quantiles with Smaller Error
Jacob Imola, Fabrizio Boninsegna, Hannah Keller +3
In the approximate quantiles problem, the goal is to output quantile estimates, the ranks of which are as close as possible to given quantiles $0 \leq q_1 \leq\dots \leq q_…