A survey on federated unlearning: Challenges, methods, and future directions

Z Liu, Y Jiang, J Shen, M Peng, KY Lam… - ACM Computing …, 2024 - dl.acm.org
In recent years, the notion of “the right to be forgotten”(RTBF) has become a crucial aspect of
data privacy for digital trust and AI safety, requiring the provision of mechanisms that support …

Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks

H Zhang, B Wu, X Yang, X Yuan, C Zhang… - arxiv preprint arxiv …, 2024 - arxiv.org
Graph unlearning has emerged as an essential tool for safeguarding user privacy and
mitigating the negative impacts of undesirable data. Meanwhile, the advent of dynamic …

Guarding the Gates: A Comprehensive Survey of Backdoor Attacks on Neural Networks

A Shah, A Ahmad, B Ali, S Anwer… - Available at SSRN … - papers.ssrn.com
The increasing use of machine learning models in various fields has unfortunately led to a
rise in backdoor attacks. These attacks secretly add harmful features to models, allowing …

Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation

H Zhang, B Wu, X Yang, Y **ngliang, X Liu… - THE WEB CONFERENCE … - openreview.net
Dynamic graph neural networks (DGNNs) have emerged and been widely deployed in
various web applications (eg, Reddit) to serve users (eg, personalized content delivery) due …