The communication cost of security and privacy in federated frequency estimation

TL;DR

This paper investigates the fundamental communication costs of secure and private federated frequency estimation, proposing optimal schemes that balance accuracy, privacy, and communication efficiency.

Contribution

It introduces an information-theoretic model for secure aggregation, characterizes the minimal communication costs under security and privacy constraints, and develops an optimal distributed differential privacy scheme.

Findings

Secure aggregation requires (n \, ext{log} \, d) ext{ bits} per user.

Naive secure schemes need (d \, ext{log} \, n) ext{ bits} per user.

The proposed scheme achieves optimal privacy-accuracy-communication trade-off.

Abstract

We consider the federated frequency estimation problem, where each user holds a private item $X_i$ from a size-$d$ domain and a server aims to estimate the empirical frequency (i.e., histogram) of $n$ items with $n \ll d$. Without any security and privacy considerations, each user can communicate its item to the server by using $\log d$ bits. A naive application of secure aggregation protocols would, however, require $d\log n$ bits per user. Can we reduce the communication needed for secure aggregation, and does security come with a fundamental cost in communication? In this paper, we develop an information-theoretic model for secure aggregation that allows us to characterize the fundamental cost of security and privacy in terms of communication. We show that with security (and without privacy) $\Omega\left( n \log d \right)$ bits per user are necessary and sufficient to allow the server to compute the frequency distribution. This is significantly smaller than the $d\log n$ bits per user needed by the naive scheme, but significantly higher than the $\log d$ bits per user needed without security. To achieve differential privacy, we construct a linear scheme based on a noisy sketch which locally perturbs the data and does not require a trusted server (a.k.a. distributed differential privacy). We analyze this scheme under $\ell_2$ and $\ell_\infty$ loss. By using our information-theoretic framework, we show that the scheme achieves the optimal accuracy-privacy trade-off with optimal communication cost, while matching the performance in the centralized case where data is stored in the central server.