Distributed Mean Estimation for Multi-Message Shuffled Privacy
Abstract
In this paper, we study distributed mean estimation (DME) under privacy and communication constraints in the multi-message shuffle model. We propose communication-efficient algorithms for privately estimating the mean of bound $\ell_2$-norm and $\ell_{\infty}$-norm norm vectors. Our algorithms are designed by giving unequal privacy at different resolutions of the vector (through binary expansion) and appropriately combining it with co-ordinate sampling. We show that our proposed algorithms achieve order-optimal privacy-communication-performance trade-offs.
Cite
Text
Girgis and Diggavi. "Distributed Mean Estimation for Multi-Message Shuffled Privacy." ICML 2023 Workshops: FL, 2023.Markdown
[Girgis and Diggavi. "Distributed Mean Estimation for Multi-Message Shuffled Privacy." ICML 2023 Workshops: FL, 2023.](https://mlanthology.org/icmlw/2023/girgis2023icmlw-distributed/)BibTeX
@inproceedings{girgis2023icmlw-distributed,
title = {{Distributed Mean Estimation for Multi-Message Shuffled Privacy}},
author = {Girgis, Antonious M. and Diggavi, Suhas},
booktitle = {ICML 2023 Workshops: FL},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/girgis2023icmlw-distributed/}
}