7 papers
SoK: Rethinking Membership Inference in Tabular Machine Learning
Cristina Pêra, Tânia Carvalho, Maxime Cordy +1
Membership Inference Attacks (MIAs) are currently the dominant approach to evaluating privacy in machine learning applications. Despite their significance, several concerns remain…
SoK: Challenges in Tabular Membership Inference Attacks
Cristina Pêra, Tânia Carvalho, Maxime Cordy +1
Membership Inference Attacks (MIAs) are currently a dominant approach for evaluating privacy in machine learning applications. Despite their significance in identifying records bel…
Privacy-Driven Network Data for Smart Cities
Tânia Carvalho, José Barata, Henish Balu +3
A smart city is essential for sustainable urban development. In addition to citizen engagement, a smart city enables connected infrastructure, data-driven decision making and smart…
Secure Visual Data Processing via Federated Learning
Pedro Santos, Tânia Carvalho, Filipe Magalhães +1
As the demand for privacy in visual data management grows, safeguarding sensitive information has become a critical challenge. This paper addresses the need for privacy-preserving…
Empowering Open Data Sharing for Social Good: A Privacy-Aware Approach
Tânia Carvalho, Luís Antunes, Cristina Costa +1
The Covid-19 pandemic has affected the world at multiple levels. Data sharing was pivotal for advancing research to understand the underlying causes and implement effective contain…
Automated Privacy-Preserving Techniques via Meta-Learning
Tânia Carvalho, Nuno Moniz, Luís Antunes
Sharing private data for learning tasks is pivotal for transparent and secure machine learning applications. Many privacy-preserving techniques have been proposed for this task aim…
Synthetic Data Outliers: Navigating Identity Disclosure
Carolina Trindade, Luís Antunes, Tânia Carvalho +1
Multiple synthetic data generation models have emerged, among which deep learning models have become the vanguard due to their ability to capture the underlying characteristics of…