7 citations · 26 across the 14 of their papers we have counts for
16 papers
OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models
Minyi Peng, Darian Gunamardi, Ivan Tjuawinata +2
Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accommodate such changes, classif…
Selective Knowledge Edit Reversal via Gated Singular Vector Shrinkage
Weifeng Jiang, Ruirui Chen, Qianren Mao +3
Knowledge editing provides an efficient way to update factual knowledge in large language models. However, malicious edits may introduce safety risks, making it necessary to revers…
Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption
Huanyi Ye, Jiale Guo, Ziyao Liu +1
RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost. In traditional architectures, RAG services are provided by a single entit…
Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions
Jiani Fan, Lwin Khin Shar, Ruichen Zhang +5
Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by government…
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
Yu Jiang, Xindi Tong, Ziyao Liu +3
Federated unlearning (FU) offers a promising solution to effectively address the need to erase the impact of specific clients' data on the global model in federated learning (FL),…
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method
Yu Jiang, Chee Wei Tan, Kwok-Yan Lam
Federated learning facilitates collaborative machine learning, enabling multiple participants to collectively develop a shared model while preserving the privacy of individual data…
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…
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…
Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning
Jiyuan Shen, Wenzhuo Yang, Zhaowei Chu +3
With the rapid development of low-cost consumer electronics and cloud computing, Internet-of-Things (IoT) devices are widely adopted for supporting next-generation distributed syst…
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…
AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories
Jiyuan Shen, Wenzhuo Yang, Kwok-Yan Lam
Training large AI models typically requires large-scale datasets in the machine learning process, making training and parameter-tuning process both time-consuming and costly. Some…
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…
MPC-enabled Privacy-Preserving Neural Network Training against Malicious Attack
Ziyao Liu, Ivan Tjuawinata, Chaoping Xing +1
The application of secure multiparty computation (MPC) in machine learning, especially privacy-preserving neural network training, has attracted tremendous attention from the resea…