Privacy Leakage via Output Label Space and Differentially Private Continual Learning
The episode discusses a paper detailing how privacy leakage occurs through output label space in continual learning systems. The core problem is that small differences between datasets can allow attackers to achieve 100% accuracy in guessing training data. The discussion focuses on two proposed solutions: O learned and O prior, designed to mitigate this risk.