Demo: Exploring Utility and Attackability Trade-offs in Local Differential Privacy

Local Differential Privacy (LDP) provides strong, formal privacy guarantees without requiring a trusted curator, making it a promising approach for privacy-preserving data collection and analysis. However, despite extensive research, practitioners may struggle to understand how to tune LDP parameters and anticipate the impact on data utility and attack risks for their specific scenarios. To address this gap, we demonstrate LDP-Toolbox, the first interactive, web-based toolbox (implemented in Python) that enables practical, analytical visualization of trade-offs between privacy loss (ε), utility loss, and vulnerability to attacks. The toolbox supports exploration of these trade-offs using real-world datasets from different domains; in this demonstration, we focus on discrete personal attributes and location-based scenarios. By providing intuitive, visual insights, LDP-Toolbox lowers the barrier to deploying LDP in real applications and helps bridge the gap between theoretical guarantees and practical adoption. The toolbox is open-source on PyPI (https://pypi.org/project/ldp-toolbox) and a video is available on our GitHub repository (https://github.com/hharcolezi/ldp-toolbox).

Recommended citation: Haoying Zhang, Abhishek K. Mishra, and Héber H. Arcolezi. 2025. Demo: Exploring Utility and Attackability Trade-offs in Local Differential Privacy. In Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security (CCS '25). Association for Computing Machinery, New York, NY, USA, 4728–4730. https://doi.org/10.1145/3719027.3760706
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