Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
(2404.10201)Abstract
We study the problem of private vector mean estimation in the shuffle model of privacy where $n$ users each have a unit vector $v{(i)} \in\mathbb{R}d$. We propose a new multi-message protocol that achieves the optimal error using $\tilde{\mathcal{O}}\left(\min(n\varepsilon2,d)\right)$ messages per user. Moreover, we show that any (unbiased) protocol that achieves optimal error requires each user to send $\Omega(\min(n\varepsilon2,d)/\log(n))$ messages, demonstrating the optimality of our message complexity up to logarithmic factors. Additionally, we study the single-message setting and design a protocol that achieves mean squared error $\mathcal{O}(dn{d/(d+2)}\varepsilon{-4/(d+2)})$. Moreover, we show that any single-message protocol must incur mean squared error $\Omega(dn{d/(d+2)})$, showing that our protocol is optimal in the standard setting where $\varepsilon = \Theta(1)$. Finally, we study robustness to malicious users and show that malicious users can incur large additive error with a single shuffler.
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