3 papers
stat.ME2026
Balancing the privacy-utility trade-off: How to draw reliable conclusions from private data
Raphaël de Fondeville
Absolute anonymization, conceived as an irreversible transformation that prevents re-identification and sensitive value disclosure, has proven to be a broken promise. Consequently,…
cs.CR2024
Lomas: A Platform for Confidential Analysis of Private Data
Damien Aymon, Dan-Thuy Lam, Lancelot Marti +3
Public services collect massive volumes of data to fulfill their missions. These data fuel the generation of regional, national, and international statistics across various sectors…
cs.CL2024
StatBot.Swiss: Bilingual Open Data Exploration in Natural Language
Farhad Nooralahzadeh, Yi Zhang, Ellery Smith +4
The potential for improvements brought by Large Language Models (LLMs) in Text-to-SQL systems is mostly assessed on monolingual English datasets. However, LLMs' performance for oth…