Forget Unlearning: Towards True Data-Deletion in Machine Learning

Rishav Chourasia, Neil Shah, Reza Shokri

Keywords: Unlearning, Differential Privacy, Data Deletion, Noisy Gradient Descent

Abstract: Unlearning has emerged as a technique to efficiently erase information of deleted records from learned models. We show, however, that the influence created by the original presence of a data point in the training set can still be detected after running certified unlearning algorithms (which can result in its reconstruction by an adversary). Thus, under realistic assumptions about the dynamics of model releases over time and in the presence of adaptive adversaries, we show that unlearning is not equivalent to data deletion and does not guarantee the "right to be forgotten." We then propose a more robust data-deletion guarantee and show that it is necessary to satisfy differential privacy to ensure true data deletion. Under our notion, we propose an accurate, computationally efficient, and secure data-deletion machine learning algorithm in the online setting based on noisy gradient descent algorithm.

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TL;DR: We show that unlearning guarantees do not ensure the "right to be forgotten" in the online setting, and we certify noisy GD as a trustworthy and utility-preserving deletion algorithm under our improved notion of data deletion.