29 citations · 129 across the 19 of their papers we have counts for
5 papers · 1 filter
Frequency Estimation of Evolving Data Under Local Differential Privacy
Héber H. Arcolezi, Carlos Pinzón, Catuscia Palamidessi +1
Collecting and analyzing evolving longitudinal data has become a common practice. One possible approach to protect the users' privacy in this context is to use local differential p…
On the Risks of Collecting Multidimensional Data Under Local Differential Privacy
Héber H. Arcolezi, Sébastien Gambs, Jean-François Couchot +1
The private collection of multiple statistics from a population is a fundamental statistical problem. One possible approach to realize this is to rely on the local model of differe…
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…
Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning
Héber H. Arcolezi
Private and public organizations regularly collect and analyze digitalized data about their associates, volunteers, clients, etc. However, because most personal data are sensitive,…