358 citations · 361 across the 10 of their papers we have counts for
11 papers
Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding
Kejia Zhang, Tianyuan Zou, Zixuan GU +1
Applications such as personalized assistance and proprietary document analysis require large language models (LLMs) to generate outputs from private data. Yet powerful LLMs typical…
E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios
Weihuang Zheng, Tianyuan Zou, Eileen Ye +5
Large Language Models (LLMs) are increasingly deployed as agents that interact with stateful environments over multiple steps: gathering hidden information, composing tool calls, a…
Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable
Tianyuan Zou, Liang Yue, Yang Liu +2
With the growing prevalence of always-on hardware such as smart glasses, body cameras, and home security systems, life-logging visual sensing is becoming inevitable, forming the ba…
PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIs
Jianqing Zhang, Yang Liu, Jie Fu +4
The rise of generative APIs has fueled interest in privacy-preserving synthetic data generation. While the Private Evolution (PE) algorithm generates Differential Privacy (DP) synt…
Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks
Yang Liu, Kejia Zhang, Bingjie Yan +11
Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-specific tasks; small models (SMs), in c…
Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion
Tianyuan Zou, Yang Liu, Peng Li +6
Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that res…