SFTC: Machine Unlearning via Selective Fine-tuning and Targeted Confusion

Vasileios Perifanis, Efstathios Karypidis, Nikos Komodakis, Pavlos S. Efraimidis

Abstract: As the importance of data privacy escalates in the modern digital era, machine learning service operators face challenges posed by the stringent privacy regulations, such as the GDPR. To cope with these challenges, the concept of machine unlearning emerges as a key solution that meets data removal requirements, while maintaining trust and transparency, thereby reducing the risk of data breaches. In this work, we present a Selective Fine-tuning and Targeted Confusion (SFTC) algorithm for machine unlearning. SFTC simultaneously performs fine-tuning on the remaining data and selectively confuses the original model by following the distribution of a biased random generator, effectively leading the forget samples’ output space to be indistinguishable from that of the original test samples. Our algorithm is evaluated on three diverse datasets for image classification and its unlearning performance is compared against six state-of-the-art unlearning algorithms. The results show that SFTC preserves a model’s original accuracy while effectively inducing forgetting on the requested data samples.