4 citations · 16 across the 9 of their papers we have counts for
10 papers
K-Bench: A Benchmark for LLM Unlearning in Agentic Deployments
Guangsheng Yu, Yanna Jiang, Qin Wang +2
Unlearning benchmarks such as TOFU and MUSE certify forgetting by reading the model's final answer, where a model that refuses to answer already counts as having forgotten. We show…
SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
Yanna Jiang, Delong Li, Haiyu Deng +4
Agentic systems increasingly rely on reusable procedural capabilities, \textit{a.k.a., agentic skills}, to execute long-horizon workflows reliably. These capabilities are callable…
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…
ByCAN: Reverse Engineering Controller Area Network (CAN) Messages from Bit to Byte Level
Xiaojie Lin, Baihe Ma, Xu Wang +4
As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications.…
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…
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
Yanna Jiang, Baihe Ma, Xu Wang +5
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comp…
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…