Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
On Reliability of Efficient Membership Inference Vulnerability Evaluation
Joonas Jälkö, Gauri Pradhan, Ossi Räisä +1
Membership inference attacks (MIAs) are popular methods for empirically assessing the leakage of sensitive information in the training data through models or statistics learned fro…
cs.LG2025
Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
Yuxuan Bai, Gauri Pradhan, Marlon Tobaben +1
With the emergence of powerful large-scale foundation models, the training paradigm is increasingly shifting from from-scratch training to transfer learning. This enables high util…
cs.LG2025
Hyperparameters in Score-Based Membership Inference Attacks
Gauri Pradhan, Joonas Jälkö, Marlon Tobaben +1
Membership Inference Attacks (MIAs) have emerged as a valuable framework for evaluating privacy leakage by machine learning models. Score-based MIAs are distinguished, in particula…