28 citations · 28 across the 10 of their papers we have counts for
11 papers
ContractLens: Latent Security Knowledge for Malicious Smart Contract Detection
Shuyi Miao, Xinyi Huang, Wangjie Qiu +4
Detecting malicious smart contracts is essential to safeguarding the Web3.0 ecosystem. However, existing auditing methods based on large language models (LLMs) largely rely on prom…
Who Bridges Safety? Identifying and Targeting Cross-Lingual Shared Safety Pathways
Shuyi Miao, Wangjie Qiu, Pengyang Shao +4
Uncovering the internal mechanisms underlying the safety capabilities of large language models (LLMs) is crucial for developing trustworthy artificial intelligence. Currently, mech…
E-MIA: Exam-Style Black-Box Membership Inference Attacks against RAG Systems
Zelin Guan, Shengda Zhuo, Zeyan Li +4
Retrieval-Augmented Generation (RAG) equips large language models (LLMs) with external evidence by retrieving documents at inference time, but it also turns the retrieval corpusint…
Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
Yuhua Wang, Qinnan Zhang, Xiaodong Li +6
Prototype-based Personalized Federated Learning (ProtoPFL) enables efficient multi-domain adaptation by communicating compact class prototypes, but directly sharing them poses priv…
UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains
Shuyi Miao, Wangjie Qiu, Shengda Zhuo +5
As cross-chain interoperability advances, decentralized finance (DeFi) protocols enable illicit funds to be reorganized into uniform liquid assets that flow throughout the cryptocu…
Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization
Yuqin Lan, Gen Li, Yuanze Hu +6
Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visual prompt attacks or gradient-…
Mask-Free Privacy Extraction and Rewriting: A Domain-Aware Approach via Prototype Learning
Xiaodong Li, Yuhua Wang, Qingchen Yu +5
Client-side privacy rewriting is crucial for deploying LLMs in privacy-sensitive domains. However, existing approaches struggle to balance privacy and utility. Full-text methods of…
Tracing Your Account: A Gradient-Aware Dynamic Window Graph Framework for Ethereum under Privacy-Preserving Services
Miao Shuyi, Qiu Wangjie, Tu Xiaofan +3
With the rapid advancement of Web 3.0 technologies, public blockchain platforms are witnessing the emergence of novel services designed to enhance user privacy and anonymity. Howev…
ContribChain: A Stress-Balanced Blockchain Sharding Protocol with Node Contribution Awareness
Xinpeng Huang, Wanqing Jie, Shiwen Zhang +8
Existing blockchain sharding protocols have focused on eliminating imbalanced workload distributions. However, even with workload balance, disparities in processing capabilities ca…
Know Your Account: Double Graph Inference-based Account De-anonymization on Ethereum
Shuyi Miao, Wangjie Qiu, Hongwei Zheng +6
The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token…
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
JiaYing Zheng, HaiNan Zhang, LingXiang Wang +3
Private data, being larger and quality-higher than public data, can greatly improve large language models (LLM). However, due to privacy concerns, this data is often dispersed in m…