1.1k citations · 2.1k across the 73 of their papers we have counts for
8 papers · 1 filter
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…
Causal Discovery Under Local Privacy
Rūta Binkytė, Carlos Pinzón, Szilvia Lestyán +3
Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled…
Online Sensitivity Optimization in Differentially Private Learning
Filippo Galli, Catuscia Palamidessi, Tommaso Cucinotta
Training differentially private machine learning models requires constraining an individual's contribution to the optimization process. This is achieved by clipping the -norm of…
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Filippo Galli, Kangsoo Jung, Sayan Biswas +2
Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their…
On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms
Héber H. Arcolezi, Selene Cerna, Catuscia Palamidessi
This paper investigates the utility gain of using Iterative Bayesian Update (IBU) for private discrete distribution estimation using data obfuscated with Locally Differentially Pri…
BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables
Ruta Binkyte, Daniele Gorla, Catuscia Palamidessi
We consider the problem of unfair discrimination between two groups and propose a pre-processing method to achieve fairness. Corrective methods like statistical parity usually lead…