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

Title:
Demo: Exploring Utility and Attackability Trade-offs in Local Differential Privacy
Authors:
Zhang, Haoying, Mishra, Abhishek, K, H. Arcolezi, Héber
Contributors:
Concevoir des technologies d'amélioration de la vie privée explicables et efficaces (PETSCRAFT), Centre Inria de Saclay, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Privacy Models, Architectures and Tools for the Information Society (PRIVATICS), Centre Inria de l'Université Grenoble Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre Inria de Lyon, Institut National de Recherche en Informatique et en Automatique (Inria), ANR-24-CE23-6239,AI-PULSE,Alignement de la protection de la vie privée, de l`utilité et de l`équité pour une IA responsable(2024), ANR-22-PECY-0002,iPoP,interdisciplinary Project on Privacy(2022), ANR-23-IACL-0006,MIAI Cluster,MIAI Cluster(2023)
Source:
CCS 2025 - ACM SIGSAC Conference on Computer and Communications Security. :4728-4730
Publisher Information:
CCSD; ACM, 2025.
Publication Year:
2025
Collection:
collection:INRIA
collection:UNIV-ORLEANS
collection:INSA-LYON
collection:INRIA-RHA
collection:INRIA-SACLAY
collection:INRIA_TEST
collection:TESTALAIN1
collection:INRIA2
collection:INRIA-RENGRE
collection:CITI
collection:INSA-GROUPE
collection:INSA-CVL
collection:UDL
collection:MIAI
collection:PNRIA
collection:ANR
collection:GS-COMPUTER-SCIENCE
collection:INRIA-LYS
collection:CYBERSCURITE
collection:IPOP
collection:ANR-IA-23
collection:ANR-IA-24
collection:ANR-IA
collection:MIAI-CLUSTER
Subject Terms:
Open Source, Privacy-Utility Trade-Off, Local Differential Privacy, CCS Concepts :• Security and privacy → Software and application security; Privacy-preserving protocols, [INFO.INFO-CR]Computer Science [cs], Cryptography and Security [cs.CR]
Subject Geographic:
Taipei, Taiwan
Original Identifier:
HAL: hal-05386311
Document Type:
Conference conferenceObject<br />Conference papers
Language:
English
ISBN:
979-84-00-71525-9
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1145/3719027.3760706
DOI:
10.1145/3719027.3760706
Access URL:
https://inria.hal.science/hal-05386311
https://inria.hal.science/hal-05386311v1/document
https://inria.hal.science/hal-05386311v1/file/2025_DEMO_LDP_Toolbox.pdf
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://creativecommons.org/licenses/by/
Accession Number:
edshal.hal.05386311v1
Database:
HAL

Further Information

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).