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Inferring Hidden User Models from the Behavior of Personalized LLM Agents
Haoyang Li, Yaxin Xiao, Qingqing Ye +2
Recent personalized LLM agents increasingly transform information retained in memory into compressed or structured representations, which we call user models, to guide later decisi…
Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks
Yaxin Xiao, Qingqing Ye, Zi Liang +4
Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through…
Dual Utilization of Perturbation for Stream Data Publication under Local Differential Privacy
Rong Du, Qingqing Ye, Yaxin Xiao +3
Stream data from real-time distributed systems such as IoT, tele-health, and crowdsourcing has become an important data source. However, the collection and analysis of user-generat…
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced Distillation
Zi Liang, Qingqing Ye, Yanyun Wang +5
Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extracti…