LDP$^3$: An Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods

TL;DR

LDP$^3$ is an open-source, extensible, and multi-threaded toolkit that facilitates the evaluation and selection of local differential privacy protocols and post-processing methods, improving utility and efficiency in privacy-preserving data collection.

Contribution

The paper introduces LDP$^3$, a comprehensive toolkit that supports modular, extensible, and multi-threaded evaluation of LDP protocols and methods, addressing evaluation challenges.

Findings

Using LDP$^3$ improves utility by selecting better protocols and post-processing methods.

Multi-threaded design significantly reduces execution times.

Experimental results validate the toolkit's effectiveness in practical scenarios.

Abstract

Local differential privacy (LDP) has become a prominent notion for privacy-preserving data collection. While numerous LDP protocols and post-processing (PP) methods have been developed, selecting an optimal combination under different privacy budgets and datasets remains a challenge. Moreover, the lack of a comprehensive and extensible LDP benchmarking toolkit raises difficulties in evaluating new protocols and PP methods. To address these concerns, this paper presents LDP$^3$ (pronounced LDP-Cube), an open-source, extensible, and multi-threaded toolkit for LDP researchers and practitioners. LDP$^3$ contains implementations of several LDP protocols, PP methods, and utility metrics in a modular and extensible design. Its modular design enables developers to conveniently integrate new protocols and PP methods. Furthermore, its multi-threaded nature enables significant reductions in execution times via parallelization. Experimental evaluations demonstrate that: (i) using LDP$^3$ to select a good protocol and post-processing method substantially improves utility compared to a bad or random choice, and (ii) the multi-threaded design of LDP$^3$ brings substantial benefits in terms of efficiency.