2 citations · 6 across the 11 of their papers we have counts for
11 papers
Your Model Is Leaking: Covert Information Transfer through LLM Residual Streams
Mingyuan Li, Yanna Jiang, Guangsheng Yu +4
Privacy-sensitive organizations may run large language models (LLMs) in restricted or air-gapped environments while exporting selected diagnostic artifacts. We show that a compromi…
MDRC: A Deployable State-Recovery Defense for Traffic Signal Control under Sensor Corruption
Mingyuan Li, Chunyu Liu, Xiao Liu +5
Traffic Signal Control (TSC) is a safety-critical cyber-physical system that relies on real-time sensing. Corrupted observations caused by adversarial perturbations or sensor failu…
Client-Cooperative Split Learning
Haiyu Deng, Yanna Jiang, Guangsheng Yu +5
Model training is increasingly offered as a service for resource-constrained data owners to build customized models. Split Learning (SL) enables such services by offloading trainin…
SoK: Credential-Based Trust Management in Decentralized Ledger Systems
Yanna Jiang, Haiyu Deng, Qin Wang +6
Trust management systems (TMS) are crucial for managing trust in distributed environments. The rise of decentralized systems and blockchain has sparked interest in credential-based…
ZAPS: A Zero-Knowledge Proof Protocol for Secure UAV Authentication with Flight Path Privacy
Shayesta Naziri, Xu Wang, Guangsheng Yu +2
The increasing deployment of Unmanned Aerial Vehicles (UAVs) for military, commercial, and logistics applications has raised significant concerns regarding flight path privacy. Con…
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…
CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories
Xiaojie Lin, Baihe Ma, Xu Wang +4
Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-ma…
PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarking
Haiyu Deng, Yanna Jiang, Guangsheng Yu +5
Our evaluation shows that PoLO achieves \textbf{99\%} watermark detection accuracy for ownership verification, while preserving data privacy and cutting verification costs to just…
BlockFUL: Enabling Unlearning in Blockchained Federated Learning
Xiao Liu, Mingyuan Li, Xu Wang +5
Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. This complexity is amplified when blockchai…
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…