178 citations · 382 across the 14 of their papers we have counts for
9 papers · 1 filter
Inferring Sensitive Attributes from Model Explanations
Vasisht Duddu, Antoine Boutet
Model explanations provide transparency into a trained machine learning model's blackbox behavior to a model builder. They indicate the influence of different input attributes to i…
Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks
Jan Aalmoes, Vasisht Duddu, Antoine Boutet
Machine learning (ML) models have been deployed for high-stakes applications. Due to class imbalance in the sensitive attribute observed in the datasets, ML models are unfair on mi…
Privacy Assessment of Federated Learning using Private Personalized Layers
Théo Jourdan, Antoine Boutet, Carole Frindel
Federated Learning (FL) is a collaborative scheme to train a learning model across multiple participants without sharing data. While FL is a clear step forward towards enforcing us…
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Embedded systems demand on-device processing of data using Neural Networks (NNs) while conforming to the memory, power and computation constraints, leading to an efficiency and acc…
Quantifying Privacy Leakage in Graph Embedding
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Graph embeddings have been proposed to map graph data to low dimensional space for downstream processing (e.g., node classification or link prediction). With the increasing collect…
DESIRE: A Third Way for a European Exposure Notification System Leveraging the best of centralized and decentralized systems
Claude Castelluccia, Nataliia Bielova, Antoine Boutet +4
This document presents an evolution of the ROBERT protocol that decentralizes most of its operations on the mobile devices. DESIRE is based on the same architecture than ROBERT but…
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks
Claude Rosin Ngueveu, Antoine Boutet, Carole Frindel +3
With the widespread adoption of the quantified self movement, an increasing number of users rely on mobile applications to monitor their physical activity through their smartphones…
The Long Road to Computational Location Privacy: A Survey
Primault Vincent, Boutet Antoine, Ben Mokhtar Sonia +1
The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services.…
Adaptive Location Privacy with ALP
Vincent Primault, Antoine Boutet, Sonia Ben Mokhtar +1
With the increasing amount of mobility data being collected on a daily basis by location-based services (LBSs) comes a new range of threats for users, related to the over-sharing o…