4 citations · 14 across the 10 of their papers we have counts for
8 papers · 1 filter
Why Neural Structural Obfuscation Can't Kill White-Box Watermarks for Good!
Yanna Jiang, Guangsheng Yu, Qingyuan Yu +2
Neural Structural Obfuscation (NSO) (USENIX Security'23) is a family of ``zero cost'' structure-editing transforms (\texttt{nso\_zero}, \texttt{nso\_clique}, \texttt{nso\_split}) t…
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: 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: 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…
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…
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…
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…
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…