Silver Linings in the Shadows: Harnessing Membership Fingerprinting for Machine Unlearning

With the rapid adoption of machine learning (ML) across sensitive domains, protecting user privacy and data security is increasingly vital. To comply with regulations like GDPR, ML frameworks must support the removal of user-contributed data and eliminate its residual influence-a process known as machine unlearning. We present a novel unlearning mechanism designed to effectively remove the impact of specific data samples from a neural network while considering the performance of the unlearned model on the primary task. We introduce a novel loss function designed to eliminate privacy-sensitive information from weights and activation values of the target model by combining the loss of target classification and the loss value of a membership approximation function to guide the unlearning process. We provide empirical evidence of the effectiveness of our unlearning approach with a theoretical upper-bound analysis through a membership inference mechanism as a proof of concept. Our results show the superior performance of our approach in terms of unlearning efficacy, latency, and fidelity of the primary task on benchmark datasets and architectures.