Private Machine Unlearning: Enhancing Data Deletion (04/26)

Machine unlearning is a process of removing the influence of specific datasets from an ML system. Introducing a framework that strategically limits the impact of data points during the learning process has advanced machine learning research by accelerating the unlearning process. This means that specific data can be removed from the model while minimizing the negative impact on performance.

However, in many real-world scenarios, carrying out such processes can be challenging. ML models inherently have a black-box nature, making it difficult to precisely understand how specific datasets influence the model during training. Therefore, accurately removing the influence of such datasets can be even more challenging. Additionally, while it’s important to completely remove the influence of certain data, there’s another issue we need to consider: data privacy.

related post: link

Privacy leakage in machine unlearning

Protecting the privacy of remaining data in unlearning is crucial. This is because users with access to both the pre-unlearning and post-unlearning models may expose which data has been deleted from the model, leading to privacy concerns and potentially opening avenues for future attacks by malicious actors. Therefore, privacy of remaining data in retrained models post-unlearning must be safeguarded.

Private machine unlearning

There are various methods to ensure the privacy of data, and among them, the addition of noise techniques can be used to protect the remaining data after unlearning. For instance, during the gradient descent process of a model where some data has been removed, adding noise ensures the privacy of the existing data, which inherently safeguards the privacy of the deleted data.

Additionally, in privacy-preserving unlearning, the following considerations must be carefully taken into account. While conventional methods ensure that deleted data cannot be accurately recovered from a single unlearned model, in real-world scenarios, it must be ensured that deleted records remain unrecoverable even after multiple releases of the model. Certain algorithms may satisfy unlearning guarantees initially, but as multiple releases progress, they might eventually reveal deleted old data.

These efforts underscore the importance of robust privacy-preserving techniques in unlearning processes, especially in an era where data privacy is paramount.

privacy preserving unlearning: link