Distributed HDMM: Scalable, Distributed, Accurate, and Differentially Private Query Workloads without a Trusted Curator

TL;DR

Distributed HDMM enables accurate, scalable, and privacy-preserving linear query answering on distributed data without a trusted curator, using secure aggregation and malicious security assumptions.

Contribution

It introduces a novel distributed protocol for high-dimensional matrix mechanisms that matches central accuracy without requiring a trusted curator.

Findings

Runs on realistic datasets with thousands of clients in under a minute.

Provides accuracy comparable to central HDMM.

Ensures security against malicious aggregators and clients.

Abstract

We present the Distributed High-Dimensional Matrix Mechanism (Distributed HDMM), a protocol for answering workloads of linear queries on distributed data that provides the accuracy of central-model HDMM without a trusted curator. Distributed HDMM leverages a secure aggregation protocol to evaluate HDMM on distributed data, and is secure in the context of a malicious aggregator and malicious clients (assuming an honest majority). Our preliminary empirical evaluation shows that Distributed HDMM can run on realistic datasets and workloads with thousands of clients in less than one minute.