3 citations · 3 across the 2 of their papers we have counts for
7 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.…
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
Zhizhi Peng, Chonghe Zhao, Taotao Wang +7
Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data priv…
Wireless Streamlet: A Spectrum-Aware and Cognitive Consensus Protocol for Edge IoT
Taotao Wang, Long Shi, Fang Liu +2
Blockchain offers a decentralized trust framework for the Internet of Things (IoT), yet deploying consensus in spectrum-congested and dynamic wireless edge IoT networks faces funda…
Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm
Taotao Wang, Yuxin Jin, Qing Yang +3
Federated Learning (FL) has emerged as a promising paradigm in distributed machine learning, enabling collaborative model training while preserving data privacy. However, despite i…
TDC-Cache: A Trustworthy Decentralized Cooperative Caching Framework for Web3.0
Jinyu Chen, Long Shi, Taotao Wang +2
The rapid growth of Web3.0 is transforming the Internet from a centralized structure to decentralized, which empowers users with unprecedented self-sovereignty over their own data.…
VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs
Guofu Liao, Taotao Wang, Shengli Zhang +3
Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy,…