Differentially Private Release of Hierarchical Origin/Destination Data with a TopDown Approach

TL;DR

This paper introduces a novel differentially private method for hierarchical origin-destination data that enhances query accuracy and reduces false positives using a TopDown approach with constrained optimization.

Contribution

It develops a new TopDown algorithm employing constrained optimization and Chebyshev distance minimization, with theoretical error guarantees and an integer optimization method to lower false positives.

Findings

High utility in real-world and synthetic datasets

Reduced false positive detection compared to existing methods

Effective for hierarchical O/D data with privacy guarantees

Abstract

This paper presents a novel method for generating differentially private tabular datasets for hierarchical data, specifically focusing on origin-destination (O/D) trips. The approach builds upon the TopDown algorithm, a constraint-based mechanism developed by the U.S. Census to incorporate invariant queries into tabular data. O/D hierarchical data refers to datasets representing trips between geographical areas organized in a hierarchical structure (e.g., region $\rightarrow$ province $\rightarrow$ city). The proposed method is designed to improve the accuracy of queries covering broader geographical areas, which are derived through aggregation. This feature provides a "zoom-in" effect on the dataset, ensuring that when zoomed back out, the overall picture is preserved. Furthermore, the approach aims to reduce false positive detection. These characteristics can strengthen practitioners' and decision-makers' confidence in adopting differential privacy datasets. The main technical contribution of this paper includes a novel TopDown algorithm that employs constrained optimization with Chebyshev distance minimization, with theoretical guarantees on the maximum absolute error. Additionally, we propose a new integer optimization algorithm that significantly reduces the incidence of false positives. The effectiveness of the proposed approach is validated using real-world and synthetic O/D datasets, demonstrating its ability to generate private data with high utility and a reduced number of false positives. Our experiments focus on O/D datasets with a single trip as a unit of privacy: nevertheless, the proposed approach supports other units of privacy and also applies to any tabular data with a hierarchical structure.