3 citations · 6 across the 5 of their papers we have counts for
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cs.CR2026
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…
cs.CR2026
From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
Zixuan Gu, Xiaojun Ye, Yang Liu
Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high compu…
cs.CR2025★ 3 cited
VFLAIR-LLM: A Comprehensive Framework and Benchmark for Split Learning of LLMs
Zixuan Gu, Qiufeng Fan, Long Sun +2
With the advancement of Large Language Models (LLMs), LLM applications have expanded into a growing number of fields. However, users with data privacy concerns face limitations in…