Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition

TL;DR

This paper proposes a scalable framework for metric differential privacy by partitioning datasets and applying Benders Decomposition to solve large linear programming problems efficiently.

Contribution

It introduces a novel dataset partitioning and Benders Decomposition approach to improve the scalability of LP-based metric differential privacy mechanisms.

Findings

Demonstrates improved scalability on geo-location, text, and synthetic datasets.

Achieves more efficient privacy-preserving data perturbation.

Outperforms traditional LP methods in large-scale scenarios.

Abstract

Metric Differential Privacy (mDP) extends the concept of Differential Privacy (DP) to serve as a new paradigm of data perturbation. It is designed to protect secret data represented in general metric space, such as text data encoded as word embeddings or geo-location data on the road network or grid maps. To derive an optimal data perturbation mechanism under mDP, a widely used method is linear programming (LP), which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in large-scale mDP. In this paper, our objective is to develop a new computation framework to enhance the scalability of the LP-based mDP. Considering the connections established by the mDP constraints among the secret records, we partition the original secret dataset into various subsets. Building upon the partition, we reformulate the LP problem for mDP and solve it via Benders Decomposition, which is composed of two stages: (1) a master program to manage the perturbation calculation across subsets and (2) a set of subproblems, each managing the perturbation derivation within a subset. Our experimental results on multiple datasets, including geo-location data in the road network/grid maps, text data, and synthetic data, underscore our proposed mechanism's superior scalability and efficiency.