activity
20152026
most citedImproving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates

29 citations · 108 across the 23 of their papers we have counts for

collaborators

25 papers

cs.AI2026

A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities

Aya Cherigui, Florent Guépin, Arnaud Legendre +1

Human mobility data are used in numerous applications, ranging from public health to urban planning. Human mobility is inherently sensitive, as it can contain information such as r…

cs.DB2025

Experiments \& Analysis of Privacy-Preserving SQL Query Sanitization Systems

Loïs Ecoffet, Veronika Rehn-Sonigo, Jean-François Couchot +1

Analytical SQL queries are essential for extracting insights from relational databases but concurrently introduce significant privacy risks by potentially exposing sensitive inform…

cs.CL2023★ 1 cited

Automatic ICD-10 Code Association: A Challenging Task on French Clinical Texts

Yakini Tchouka, Jean-François Couchot, David Laiymani +2

Automatically associating ICD codes with electronic health data is a well-known NLP task in medical research. NLP has evolved significantly in recent years with the emergence of pr…

cs.CR2022

An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents

Yakini Tchouka, Jean-François Couchot, David Laiymani

Unstructured textual data is at the heart of healthcare systems. For obvious privacy reasons, these documents are not accessible to researchers as long as they contain personally i…

cs.DS2022

In-stream Probabilistic Cardinality Estimation for Bloom Filters

Remy Scholler, Jean-Francois Couchot, Oumaima Alaoui-Ismaili +2

The amount of data coming from different sources such as IoT-sensors, social networks, cellular networks, has increased exponentially during the last few years. Probabilistic Data…

cs.LG2022★ 10 cited

Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?

Héber H. Arcolezi, Jean-François Couchot, Denis Renaud +2

This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-o…