9 citations · 36 across the 9 of their papers we have counts for
10 papers
AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions
Chen Chen, Xueluan Gong, Ziyao Liu +3
AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. With the rapid proliferation of AI and especially with the recent advancemen…
Guaranteeing Data Privacy in Federated Unlearning with Dynamic User Participation
Ziyao Liu, Yu Jiang, Weifeng Jiang +3
Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of Federated Learning (FL) users' data from trained global FL models. A straightforward F…
Privacy-Preserving Federated Unlearning with Certified Client Removal
Ziyao Liu, Huanyi Ye, Yu Jiang +4
In recent years, Federated Unlearning (FU) has gained attention for addressing the removal of a client's influence from the global model in Federated Learning (FL) systems, thereby…
Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
Jingyu Wang, Niantai Jing, Ziyao Liu +4
In copy-move tampering operations, perpetrators often employ techniques, such as blurring, to conceal tampering traces, posing significant challenges to the detection of object-lev…
A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles
Jiani Fan, Minrui Xu, Ziyao Liu +4
Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models. Mobile AIGC services in the Internet of Vehicles (IoV)…
Threats, Attacks, and Defenses in Machine Unlearning: A Survey
Ziyao Liu, Huanyi Ye, Chen Chen +2
Machine Unlearning (MU) has recently gained considerable attention due to its potential to achieve Safe AI by removing the influence of specific data from trained Machine Learning…
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
Yu Jiang, Jiyuan Shen, Ziyao Liu +2
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model. Although various methods exist for detectin…
A Survey on Federated Unlearning: Challenges, Methods, and Future Directions
Ziyao Liu, Yu Jiang, Jiyuan Shen +4
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 mechanism…
Privacy-Preserving Aggregation in Federated Learning: A Survey
Ziyao Liu, Jiale Guo, Wenzhuo Yang +3
Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PP…
Understanding Security in Smart City Domains From the ANT-centric Perspective
Jiani Fan, Wenzhuo Yang, Ziyao Liu +4
A city is a large human settlement that serves the people who live there, and a smart city is a concept of how cities might better serve their residents through new forms of techno…