Segmented Private Data Aggregation in the Multi-message Shuffle Model

TL;DR

This paper introduces a segmented private data aggregation framework in the multi-message shuffle model of differential privacy, allowing flexible privacy levels and significantly improving accuracy while maintaining privacy protections.

Contribution

It pioneers segmented privacy protection in the multi-message shuffle model, enabling flexible privacy levels and optimizing privacy-utility trade-offs for better data aggregation.

Findings

Achieves about 50% reduction in estimation error.

Provides a flexible privacy protection framework.

Enhances privacy and utility simultaneously.

Abstract

The shuffle model of differential privacy (DP) offers compelling privacy-utility trade-offs in decentralized settings (e.g., internet of things, mobile edge networks). Particularly, the multi-message shuffle model, where each user may contribute multiple messages, has shown that accuracy can approach that of the central model of DP. However, existing studies typically assume a uniform privacy protection level for all users, which may deter conservative users from participating and prevent liberal users from contributing more information, thereby reducing the overall data utility, such as the accuracy of aggregated statistics. In this work, we pioneer the study of segmented private data aggregation within the multi-message shuffle model of DP, introducing flexible privacy protection for users and enhanced utility for the aggregation server. Our framework not only protects users' data but also anonymizes their privacy level choices to prevent potential data leakage from these choices. To optimize the privacy-utility-communication trade-offs, we explore approximately optimal configurations for the number of blanket messages and conduct almost tight privacy amplification analyses within the shuffle model. Through extensive experiments, we demonstrate that our segmented multi-message shuffle framework achieves a reduction of about 50\% in estimation error compared to existing approaches, significantly enhancing both privacy and utility.