Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

This article examines the impact of new AI‐related technologies in data mining and big data
on important research questions in crime analytics. Because the field is so broad, the review …

There is a growing concern that learned conditional generative models may output samples
that are substantially similar to some copyrighted data $ C $ that was in their training set. We …

Differentially private stochastic gradient descent (DP-SGD) adds noise to gradients in back-
propagation, safeguarding training data from privacy leakage, particularly membership …

Machine learning models exhibit two seemingly contradictory phenomena: training data
memorization, and various forms of forgetting. In memorization, models overfit specific …

Two-party split learning is a popular technique for learning a model across feature-
partitioned data. In this work, we explore whether it is possible for one party to steal the …

We consider the privacy-preserving machine learning (ML) setting where the trained model
must satisfy differential privacy (DP) with respect to the labels of the training examples. We …

Differential privacy is the de-facto technique for protecting the individuals in the training
dataset and the learning models in deep learning. However, the technique presents two …

Split learning is privacy-preserving distributed learning that has gained momentum recently.
It also faces new security challenges. FSHA [37] is a serious threat to split learning. In FSHA …

Differentially private (DP) learning, notably DP stochastic gradient descent (DP-SGD), has
limited applicability in fine-tuning gigantic pre-trained language models (LMs) for natural …