358 citations · 371 across the 11 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…
FuseGen: PLM Fusion for Data-generation based Zero-shot Learning
Tianyuan Zou, Yang Liu, Peng Li +3
Data generation-based zero-shot learning, although effective in training Small Task-specific Models (STMs) via synthetic datasets generated by Pre-trained Language Models (PLMs), i…
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
Tianyuan Zou, Zixuan Gu, Yu He +3
Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish coopera…
Mutual Information Regularization for Vertical Federated Learning
Tianyuan Zou, Yang Liu, Ya-Qin Zhang
Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable collaborative learning while protecting data privacy and safety. However, previous works s…
Vertical Federated Learning: Concepts, Advances and Challenges
Yang Liu, Yan Kang, Tianyuan Zou +6
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models w…
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning
Yang Liu, Tianyuan Zou, Yan Kang +4
In a vertical federated learning (VFL) scenario where features and model are split into different parties, communications of sample-specific updates are required for correct gradie…