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arXiv · 2406.16305

On Computing Pairwise Statistics with Local Differential Privacy

Badih Ghazi·Pritish Kamath·Ravi Kumar·Pasin Manurangsi·Adam Sealfon
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Abstract

We study the problem of computing pairwise statistics, i.e., ones of the form $\binom{n}{2}^{-1} \sum_{i \ne j} f(x_i, x_j)$, where $x_i$ denotes the input to the $i$th user, with differential privacy (DP) in the local model. This formulation captures important metrics such as Kendall's $\tau$ coefficient, Area Under Curve, Gini's mean difference, Gini's entropy, etc. We give several novel and generic algorithms for the problem, leveraging techniques from DP algorithms for linear queries.

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Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon. 2024-06-24. On Computing Pairwise Statistics with Local Differential Privacy. https://arxiv.org/abs/2406.16305

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