Alexander Warnecke (TU Braunschweig), Lukas Pirch (TU Braunschweig), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Konrad Rieck (TU Braunschweig)

Removing information from a machine learning model is a non-trivial task that requires to partially revert the training process. This task is unavoidable when sensitive data, such as credit card numbers or passwords, accidentally enter the model and need to be removed afterwards. Recently, different concepts for machine unlearning have been proposed to address this problem. While these approaches are effective in removing individual data points, they do not scale to scenarios where larger groups of features and labels need to be reverted. In this paper, we propose the first method for unlearning features and labels. Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters. It enables to adapt the influence of training data on a learning model retrospectively, thereby correcting data leaks and privacy issues. For learning models with strongly convex loss functions, our method provides certified unlearning with theoretical guarantees. For models with non-convex losses, we empirically show that unlearning features and labels is effective and significantly faster than other strategies.

View More Papers

OBI: a multi-path oblivious RAM for forward-and-backward-secure searchable encryption

Zhiqiang Wu (Changsha University of Science and Technology), Rui Li (Dongguan University of Technology)

Read More

On the Anonymity of Peer-To-Peer Network Anonymity Schemes Used...

Piyush Kumar Sharma (imec-COSIC, KU Leuven), Devashish Gosain (Max Planck Institute for Informatics), Claudia Diaz (Nym Technologies, SA and imec-COSIC, KU Leuven)

Read More

Brokenwire: Wireless Disruption of CCS Electric Vehicle Charging

Sebastian Köhler (University of Oxford), Richard Baker (University of Oxford), Martin Strohmeier (armasuisse Science + Technology), Ivan Martinovic (University of Oxford)

Read More

VASP: V2X Application Spoofing Platform

Mohammad Raashid Ansari, Jonathan Petit, Jean-Philippe Monteuuis, Cong Chen (Qualcomm Technologies, Inc.)

Read More