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

• URL: http://arxiv.org/abs/2507.05872v1
• Date: Tue, 08 Jul 2025 10:51:42 GMT
• Title: LDP$^3$: An Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods
• Authors: Berkay Kemal Balioglu, Alireza Khodaie, Mehmet Emre Gursoy,
• Abstract summary: Local differential privacy (LDP) has become a prominent notion for privacy-preserving data collection.<n>This paper presents LDP$3$, an open-source, benchmarking, and multi-threaded toolkit for LDP researchers and practitioners.
• Score: 1.0486921990935787
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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