12 citations · 25 across the 10 of their papers we have counts for
10 papers
SoK: Semantic Privacy in Large Language Models
Baihe Ma, Yanna Jiang, Xu Wang +8
As Large Language Models (LLMs) are increasingly deployed in sensitive domains, traditional data privacy measures prove inadequate for protecting information that is implicit, cont…
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth
Ying Zhuansun, Dandan Li, Xiaohong Huang +1
Federated learning can train models without directly providing local data to the server. However, the frequent updating of the local model brings the problem of large communication…
Maximizing NFT Incentives: References Make You Rich
Guangsheng Yu, Qin Wang, Caijun Sun +3
In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we come across…
TBDD: A New Trust-based, DRL-driven Framework for Blockchain Sharding in IoT
Zixu Zhang, Guangsheng Yu, Caijun Sun +7
Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel sha…
Split Unlearning
Guangsheng Yu, Yanna Jiang, Qin Wang +5
We introduce Split Unlearning, a novel machine unlearning technology designed for Split Learning (SL), enabling the first-ever implementation of Sharded, Isolated, Sliced, and Aggr…
A Secure Aggregation for Federated Learning on Long-Tailed Data
Yanna Jiang, Baihe Ma, Xu Wang +4
As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. I…
Location Privacy Threats and Protections in 6G Vehicular Networks: A Comprehensive Review
Baihe Ma, Xu Wang, Xiaojie Lin +8
Location privacy is critical in vehicular networks, where drivers' trajectories and personal information can be exposed, allowing adversaries to launch data and physical attacks th…
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
Guangsheng Yu, Xu Wang, Caijun Sun +5
Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the ov…
An Empirical Study on Snapshot DAOs
Qin Wang, Guangsheng Yu, Yilin Sai +4
Decentralized Autonomous Organization (DAO) is an organization constructed by automatically executed rules such as via smart contracts, holding features of the permissionless commi…
Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learning
Guangsheng Yu, Xu Wang, Ping Yu +3
Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties for edge a…