Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the right to be forgotten of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia. While the area has developed rapidly, there is a lack of comprehensive surveys to capture the latest advancements. Recognizing this shortage, we conduct an extensive exploration to map the landscape of machine unlearning including the (fine-grained) taxonomy of unlearning algorithms under centralized and distributed settings, debate on approximate unlearning, verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. The survey concludes by outlining potential directions for future research, hoping to serve as a guide for interested scholars.

PPA: Preference Profiling Attack Against Federated Learning

Federated learning (FL) trains a global model across a number of decentralized participants, each with a local dataset. Compared to traditional centralized learning, FL does not require direct local datasets access and thus mitigates data security and privacy concerns. However, data privacy concerns for FL still exist due to inference attacks, including known membership inference, property inference, and data inversion. In this work, we reveal a new type of privacy inference attack, coined Preference Profiling Attack (PPA), that accurately profiles private preferences of a local user. In general, the PPA can profile top-k, especially for top-1, preferences contingent on the local user's characteristics. Our key insight is that the gradient variation of a local user's model has a distinguishable sensitivity to the sample proportion of a given class, especially the majority/minority class. By observing a user model's gradient sensitivity to a class, the PPA can profile the sample proportion of the class in the user's local dataset and thus the user's preference of the class is exposed. The inherent statistical heterogeneity of FL further facilitates the PPA. We have extensively evaluated the PPA's effectiveness using four datasets from the image domains of MNIST, CIFAR10, Products-10K and RAF-DB. Our results show that the PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10, respectively. More importantly, in the real-world commercial scenarios of shopping (i.e., Products-10K) and the social network (i.e., RAF-DB), the PPA gains a top-1 attack accuracy of 78% in the former case to infer the most ordered items, and 88% in the latter case to infer a victim user's emotions. Although existing countermeasures such as dropout and differential privacy protection can lower the PPA's accuracy to some extent, they unavoidably incur notable global model deterioration.