1 citations · 1 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
Noise-Aware Differentially Private Variational Inference
Talal Alrawajfeh, Joonas Jälkö, Antti Honkela
Differential privacy (DP) provides robust privacy guarantees for statistical inference, but this can lead to unreliable results and biases in downstream applications. While several…
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…
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning
Marlon Tobaben, Hibiki Ito, Joonas Jälkö +2
Membership inference attacks (MIAs) are used to test practical privacy of machine learning models. MIAs complement formal guarantees from differential privacy (DP) under a more rea…