A survey on federated unlearning: Challenges, methods, and future directions
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 …
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
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 …
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 …
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
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 …
various web applications (eg, Reddit) to serve users (eg, personalized content delivery) due …