15 citations · 56 across the 19 of their papers we have counts for
4 papers · 1 filter
Empirical Calibration and Metric Differential Privacy in Language Models
Pedro Faustini, Natasha Fernandes, Annabelle McIver +1
NLP models trained with differential privacy (DP) usually adopt the DP-SGD framework, and privacy guarantees are often reported in terms of the privacy budget . However, doe…
Comparing privacy notions for protection against reconstruction attacks in machine learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy pres…
Bayes' capacity as a measure for reconstruction attacks in federated learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy…
Directional Privacy for Deep Learning
Pedro Faustini, Natasha Fernandes, Shakila Tonni +2
Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying privacy in the training of deep learning models. It applies isotropic Gaussian noise to gra…