29 citations · 129 across the 19 of their papers we have counts for
6 papers · 1 filter
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Adda Akram Bendoukha, Heber Hwang Arcolezi, Nesrine Kaaniche +1
Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the presence of non-IID data, whic…
Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness
Héber H. Arcolezi, Mina Alishahi, Adda-Akram Bendoukha +1
Machine learning (ML) algorithms are heavily based on the availability of training data, which, depending on the domain, often includes sensitive information about data providers.…
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
Karima Makhlouf, Tamara Stefanovic, Heber H. Arcolezi +1
Machine learning (ML) algorithms rely primarily on the availability of training data, and, depending on the domain, these data may include sensitive information about the data prov…
On the Impact of Multi-dimensional Local Differential Privacy on Fairness
Karima Makhlouf, Heber H. Arcolezi, Sami Zhioua +2
Automated decision systems are increasingly used to make consequential decisions in people's lives. Due to the sensitivity of the manipulated data as well as the resulting decision…
(Local) Differential Privacy has NO Disparate Impact on Fairness
Héber H. Arcolezi, Karima Makhlouf, Catuscia Palamidessi
In recent years, Local Differential Privacy (LDP), a robust privacy-preserving methodology, has gained widespread adoption in real-world applications. With LDP, users can perturb t…
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…