29 citations · 139 across the 31 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python
Héber H. Arcolezi, Jean-François Couchot, Sébastien Gambs +2
This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving l…