190 citations · 289 across the 21 of their papers we have counts for
5 papers · 1 filter
Private Multi-Winner Voting for Machine Learning
Adam Dziedzic, Christopher A Choquette-Choo, Natalie Dullerud +6
Private multi-winner voting is the task of revealing -hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine lea…
On the reversibility of adversarial attacks
Chau Yi Li, Ricardo Sánchez-Matilla, Ali Shahin Shamsabadi +2
Adversarial attacks modify images with perturbations that change the prediction of classifiers. These modified images, known as adversarial examples, expose the vulnerabilities of…
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation
Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet +1
In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Pert…
Differentially Private Speaker Anonymization
Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet +5
Sharing real-world speech utterances is key to the training and deployment of voice-based services. However, it also raises privacy risks as speech contains a wealth of personal da…
Tubes Among Us: Analog Attack on Automatic Speaker Identification
Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi +4
Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning. Yet, machine learning has proven to be vulnerable to adversarial…