424 citations · 1.1k across the 25 of their papers we have counts for
25 papers
PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
Yi Liu, Weixiang Han, Chengjun Cai +2
With the rise of large language models, service providers offer language models as a service, enabling users to fine-tune customized models via uploaded private datasets. However,…
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities
Yi Liu, Cong Wang, Xingliang Yuan
The Web of Things (WoT) enhances interoperability across web-based and ubiquitous computing platforms while complementing existing IoT standards. The multimodal Federated Learning…
Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts
Yi Liu, Chengjun Cai, Xiaoli Zhang +2
Large Vision Language Models (VLMs) extend and enhance the perceptual abilities of Large Language Models (LLMs). Despite offering new possibilities for LLM applications, these adva…
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning
Yi Liu, Cong Wang, Xingliang Yuan
Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients manipulate the global model by modifying local datasets or sending manipulated model update…
Leakage-Abuse Attacks Against Forward and Backward Private Searchable Symmetric Encryption
Lei Xu, Leqian Zheng, Chengzhi Xu +2
Dynamic searchable symmetric encryption (DSSE) enables a server to efficiently search and update over encrypted files. To minimize the leakage during updates, a security notion nam…
Training-free Lexical Backdoor Attacks on Language Models
Yujin Huang, Terry Yue Zhuo, Qiongkai Xu +3
Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor…
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
He Zhang, Xingliang Yuan, Shirui Pan
Graph neural networks (GNNs) have gained significant attraction due to their expansive real-world applications. To build trustworthy GNNs, two aspects - fairness and privacy - have…
Penny Wise and Pound Foolish: Quantifying the Risk of Unlimited Approval of ERC20 Tokens on Ethereum
Dabao Wang, Hang Feng, Siwei Wu +3
The prosperity of decentralized finance motivates many investors to profit via trading their crypto assets on decentralized applications (DApps for short) of the Ethereum ecosystem…
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang, Bang Wu, Xingliang Yuan +3
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation syst…
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining
Yi Liu, Lei Xu, Xingliang Yuan +2
In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm named \textit{machine unlearning}, which enables data holders to proactively erase…
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization
Yifeng Zheng, Shangqi Lai, Yi Liu +3
Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features th…
Projective Ranking-based GNN Evasion Attacks
He Zhang, Xingliang Yuan, Chuan Zhou +1
Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial attacks. Two primary limitations of the current evas…
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
Bang Wu, Xiangwen Yang, Shirui Pan +1
Graph Neural Networks (GNNs) are widely adopted to analyse non-Euclidean data, such as chemical networks, brain networks, and social networks, modelling complex relationships and i…
DeFiRanger: Detecting Price Manipulation Attacks on DeFi Applications
Siwei Wu, Dabao Wang, Jianting He +5
The rapid growth of Decentralized Finance (DeFi) boosts the Ethereum ecosystem. At the same time, attacks towards DeFi applications (apps) are increasing. However, to the best of o…
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses
Yi Liu, Xingliang Yuan, Ruihui Zhao +3
Semi-supervised Federated Learning (SSFL) has recently drawn much attention due to its practical consideration, i.e., the clients may only have unlabeled data. In practice, these S…
Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization
Bang Wu, Xiangwen Yang, Shirui Pan +1
Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker…
Towards A First Step to Understand Flash Loan and Its Applications in DeFi Ecosystem
Dabao Wang, Siwei Wu, Ziling Lin +5
Flash Loan, as an emerging service in the decentralized finance ecosystem, allows users to request a non-collateral loan. While providing convenience, it also enables attackers to…
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
Yi Liu, Xingliang Yuan, Zehui Xiong +3
As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally belie…
ShieldDB: An Encrypted Document Database with Padding Countermeasures
Viet Vo, Xingliang Yuan, Shi-Feng Sun +3
The security of our data stores is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of…
Accelerating Forward and Backward Private Searchable Encryption Using Trusted Execution
Viet Vo, Shangqi Lai, Xingliang Yuan +3
Searchable encryption (SE) is one of the key enablers for building encrypted databases. It allows a cloud server to search over encrypted data without decryption. Dynamic SE additi…
Towards Practical Encrypted Network Traffic Pattern Matching for Secure Middleboxes
Shangqi Lai, Xingliang Yuan, Shi-Feng Sun +4
Network Function Virtualisation (NFV) advances the adoption of composable software middleboxes. Accordingly, cloud data centres become major NFV vendors for enterprise traffic proc…
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets
Shangqi Lai, Xingliang Yuan, Amin Sakzad +3
Outlier detection is widely used in practice to track the anomaly on incremental datasets such as network traffic and system logs. However, these datasets often involve sensitive i…
Defeating Misclassification Attacks Against Transfer Learning
Bang Wu, Shuo Wang, Xingliang Yuan +3
Transfer learning is prevalent as a technique to efficiently generate new models (Student models) based on the knowledge transferred from a pre-trained model (Teacher model). Howev…
GraphSE: An Encrypted Graph Database for Privacy-Preserving Social Search
Shangqi Lai, Xingliang Yuan, Shi-Feng Sun +3
In this paper, we propose GraphSE, an encrypted graph database for online social network services to address massive data breaches. GraphSE preserves the functionality of s…