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Fabrizio Boninsegna

2 accepted papers

2025

Differentially Private Quantiles with Smaller Error

NeurIPS 2025poster

In the approximate quantiles problem, the goal is to output $m$ quantile estimates, the ranks of which are as close as possible to $m$ given quantiles $0 \leq q_1 \leq\dots \leq q_m \leq 1$. We present a mechanism for approximate quantiles that satisfies $\varepsilon$-differential privacy for a dat…

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2025

Lightweight Protocols for Distributed Private Quantile Estimation

ICML 2025spotlight

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 consider two \emph{adaptive} algorithms for estimating one quantile (e.g.~the median) when each user holds a single data…