Machine Unlearning: Solutions and Challenges

arXiv:2308.07061v2 Announce Type: replace
Abstract: Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and analysis of the solutions in machine unlearning. We categorize existing solutions into exact unlearning approaches that remove data influence thoroughly and approximate unlearning approaches …

abstract arxiv breaches challenges cs.ai cs.lg data influence machine machine learning machine learning models paper performance privacy risks security security vulnerabilities solutions taxonomy training training data type unlearning vulnerabilities