Differentially Private Grids for Geospatial Data

TL;DR

This paper introduces a new differentially private grid-based approach for geospatial data that adaptively adjusts partition granularity, outperforming existing recursive partitioning methods in accuracy and efficiency.

Contribution

It proposes a novel adaptive-grid method for differentially private data synthesis, with a systematic approach for choosing grid size and extensive experimental validation.

Findings

The adaptive grid method outperforms state-of-the-art techniques in accuracy.

The proposed grid size selection method balances noise and non-uniformity errors effectively.

Experimental results show significant improvements on real-world datasets.

Abstract

In this paper, we tackle the problem of constructing a differentially private synopsis for two-dimensional datasets such as geospatial datasets. The current state-of-the-art methods work by performing recursive binary partitioning of the data domains, and constructing a hierarchy of partitions. We show that the key challenge in partition-based synopsis methods lies in choosing the right partition granularity to balance the noise error and the non-uniformity error. We study the uniform-grid approach, which applies an equi-width grid of a certain size over the data domain and then issues independent count queries on the grid cells. This method has received no attention in the literature, probably due to the fact that no good method for choosing a grid size was known. Based on an analysis of the two kinds of errors, we propose a method for choosing the grid size. Experimental results validate our method, and show that this approach performs as well as, and often times better than, the state-of-the-art methods. We further introduce a novel adaptive-grid method. The adaptive grid method lays a coarse-grained grid over the dataset, and then further partitions each cell according to its noisy count. Both levels of partitions are then used in answering queries over the dataset. This method exploits the need to have finer granularity partitioning over dense regions and, at the same time, coarse partitioning over sparse regions. Through extensive experiments on real-world datasets, we show that this approach consistently and significantly outperforms the uniform-grid method and other state-of-the-art methods.