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cs.CR2026
Privacy by Postprocessing the Discrete Laplace Mechanism
Quentin Hillebrand, Jacob Imola, Rasmus Pagh +1
We show that an "old dog", the classical discrete Laplace (aka.~geometric) mechanism, can "perform new tricks": 1. It can be post-processed to yield a simple, unbiased estimator of…
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.CR2025
Lightweight Protocols for Distributed Private Quantile Estimation
Anders Aamand, Fabrizio Boninsegna, Abigail Gentle +2
Distributed data analysis is a large and growing field driven by a massive proliferation of user devices, and by privacy concerns surrounding the centralised storage of data. We co…