Differentially private (DP) training methods like DP-SGD can protect sensitive training data by ensuring that ML models will not reveal...
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As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes...
The recently accentuated features of augmenting conventional wireless networks with high altitude platform systems (HAPS) have fueled a plethora of...
Differentially private (DP) training methods like DP-SGD can protect sensitive training data by ensuring that ML models will not reveal...
Privacy attacks on Machine Learning (ML) models often focus on inferring the existence of particular data points in the training...
As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes...
Privacy attacks on Machine Learning (ML) models often focus on inferring the existence of particular data points in the training...
As a major component of online crime, email-based fraud is a threat that causes substantial economic losses every year. To...
Differentially private (DP) training methods like DP-SGD can protect sensitive training data by ensuring that ML models will not reveal...
In this paper, we initiate the study of local model reconstruction attacks for federated learning, where a honest-but-curious adversary eavesdrops...