AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy

TL;DR

AHEAD is an adaptive hierarchical method for range queries under local differential privacy that dynamically optimizes data structure to reduce noise and improve utility across various privacy settings.

Contribution

The paper introduces AHEAD, a novel adaptive hierarchical decomposition protocol that dynamically controls tree structure to enhance utility in LDP-based range queries.

Findings

AHEAD outperforms existing methods in low and high dimensional scenarios.

AHEAD maintains high utility with rigorous LDP guarantees.

Guidelines for parameter selection improve practical deployment.

Abstract

For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis. However, existing LDP-based range query approaches are limited by their properties, i.e., collecting user data according to a pre-defined structure. These static frameworks would incur excessive noise added to the aggregated data especially in the low privacy budget setting. In this work, we propose an Adaptive Hierarchical Decomposition (AHEAD) protocol, which adaptively and dynamically controls the built tree structure, so that the injected noise is well controlled for maintaining high utility. Furthermore, we derive a guideline for properly choosing parameters for AHEAD so that the overall utility can be consistently competitive while rigorously satisfying LDP. Leveraging multiple real and synthetic datasets, we extensively show the effectiveness of AHEAD in both low and high dimensional range query scenarios, as well as its advantages over the state-of-the-art methods. In addition, we provide a series of useful observations for deploying AHEAD in practice.