NeurIPS 2023poster3 citations
On Computing Pairwise Statistics with Local Differential Privacy
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
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.
differential privacylocal differential privacypairwise statistics
BibTeX
@inproceedings{
ghazi2023on,
title={On Computing Pairwise Statistics with Local Differential Privacy},
author={Badih Ghazi and Pritish Kamath and Ravi Kumar and Pasin Manurangsi and Adam Sealfon},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=1GxKVprbwM}
}