Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning

arXiv:2310.18574v2 Announce Type: replace-cross
Abstract: Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influence of specific data samples on a given model without the need to retrain it from scratch. Accordingly, existing methods focus on maximizing user privacy protection. However, there are different degrees of privacy regulations for each real-world web-based application. Exploring the full spectrum of trade-offs between privacy, model utility, …

arxiv breaking cs.ai cs.cr cs.lg efficiency machine privacy type unlearning utility via