most citedTransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment

9 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.PL2024

SecCoder: Towards Generalizable and Robust Secure Code Generation

Boyu Zhang, Tianyu Du, Junkai Tong +5

After large models (LMs) have gained widespread acceptance in code-related tasks, their superior generative capacity has greatly promoted the application of the code LM. Neverthele…

cs.CL2024★ 2 cited

Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding

Yanming Liu, Xinyue Peng, Jiannan Cao +6

Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy context…

cs.CR2024

DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models

Yanming Liu, Xinyue Peng, Yuwei Zhang +10

Large language models have repeatedly shown outstanding performance across diverse applications. However, deploying these models can inadvertently risk user privacy. The significan…

cs.AI2024★ 1 cited

Tool-Planner: Task Planning with Clusters across Multiple Tools

Yanming Liu, Xinyue Peng, Jiannan Cao +6

Large language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the p…

cs.CR2024★ 9 cited

TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment

Qinfeng Li, Zhiqiang Shen, Zhenghan Qin +4

Proprietary large language models (LLMs) have been widely applied in various scenarios. Additionally, deploying LLMs on edge devices is trending for efficiency and privacy reasons.…