8 citations · 15 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
Privacy Leakage via Output Label Space and Differentially Private Continual Learning
Marlon Tobaben, Talal Alrawajfeh, Marcus Klasson +3
Differential privacy (DP) is a formal privacy framework that enables training machine learning (ML) models while protecting individuals' data. As pointed out by prior work, ML mode…
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
Efficient and Scalable Implementation of Differentially Private Deep Learning without Shortcuts
Sebastian Rodriguez Beltran, Marlon Tobaben, Joonas Jälkö +2
Differentially private stochastic gradient descent (DP-SGD) is the standard algorithm for training machine learning models under differential privacy (DP). The most common DP-SGD p…
Noise-Aware Differentially Private Regression via Meta-Learning
Ossi Räisä, Stratis Markou, Matthew Ashman +4
Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the go…