The Conundrum Of User Data Deletion From ML Models

Researchers at Stanford and UC San Diego propose a novel approach for rapidly removing sensitive user data from machine learning (ML) models. They provide a method for evaluating when models developed from specific user data can no longer be used and address the issue of efficiently eliminating individual data from ML models after they have been trained. The only way to erase a person's data from many basic ML models is to completely retrain the model on the remaining data. As a result, researchers are investigating ML algorithms capable of efficiently removing data. According to James Zou, a professor of biomedical data science at Stanford University and an expert in artificial intelligence, achieving optimal data erasure in real-time is difficult.