Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics

TL;DR

This paper introduces a new numerical aggregation protocol for federated analytics that enhances privacy and efficiency, making it easier to implement and more trustworthy for edge device data collection.

Contribution

The work presents a novel, practical numerical aggregation method that improves privacy guarantees and empirical performance over existing solutions, with added privacy metering capabilities.

Findings

Empirically outperforms prior aggregation methods

Provides comparable local differential privacy guarantees

Supports privacy metering for enhanced control

Abstract

Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of solutions are being explored but have not yet won the public's trust and full backing of mobile platforms. In this work, we propose numerical aggregation protocols that empirically improve upon prior art, while providing comparable local differential privacy guarantees. Sharing a single private bit per value supports privacy metering that enable privacy controls and guarantees that are not covered by differential privacy. We put emphasis on the ease of implementation, compatibility with existing methods, and compelling empirical performance.