5 citations · 7 across the 8 of their papers we have counts for
9 papers
ZK-AMS: Credibly Anonymous Admission for Web 3.0 Platforms via Recursive Proof Aggregation
Zibin Lin, Taotao Wang, Shengli Zhang +3
Web 3.0 platforms need an onboarding mechanism that can admit real users at scale without forcing them to reveal identity documents or pay one on-chain verification cost per user.…
Privacy-Preserving Semantic Communication over Wiretap Channels with Learnable Differential Privacy
Weixuan Chen, Qianqian Yang, Shuo Shao +3
While semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information, it also raises critical privacy concerns. Many existing secure SemC…
A Framework to Prevent Biometric Data Leakage in the Immersive Technologies Domain
Keshav Sood, Iynkaran Natgunanathan, Uthayasanker Thayasivam +3
Doubtlessly, the immersive technologies have potential to ease people's life and uplift economy, however the obvious data privacy risks cannot be ignored. For example, a participan…
Data Sharing, Privacy and Security Considerations in the Energy Sector: A Review from Technical Landscape to Regulatory Specifications
Shiliang Zhang, Sabita Maharjan, Lee Andrew Bygrave +1
Decarbonization, decentralization and digitalization are the three key elements driving the twin energy transition. The energy system is evolving to a more data driven ecosystem, l…
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning
Wenqiang Ruan, Xin Lin, Ruisheng Zhou +3
Multi-party computation (MPC) based machine learning, referred to as multi-party learning (MPL), has become an important technology for utilizing data from multiple parties with pr…
Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration
Xuhan Zuo, Minghao Wang, Tianqing Zhu +2
Large language models (LLMs) have transformed the way computers understand and process human language, but using them effectively across different organizations remains still diffi…
New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook
Meng Yang, Tianqing Zhu, Chi Liu +3
Thanks to the explosive growth of data and the development of computational resources, it is possible to build pre-trained models that can achieve outstanding performance on variou…
Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning
Xuhan Zuo, Minghao Wang, Tianqing Zhu +4
The development of Large Language Models (LLMs) faces a significant challenge: the exhausting of publicly available fresh data. This is because training a LLM needs a large demandi…
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
Xuhan Zuo, Minghao Wang, Tianqing Zhu +3
With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imp…