3 papers
cs.CR2026
Towards Privacy-Preserving LLM Inference via Covariant Obfuscation (Technical Report)
Yu Lin, Qizhi Zhang, Wenqiang Ruan +6
The rapid development of large language models (LLMs) has driven the widespread adoption of cloud-based LLM inference services, while also bringing prominent privacy risks associat…
cs.CR2025
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning
Wenqiang Ruan, Xin Lin, Ruisheng Zhou +3
Multi-party computation (MPC) based machine learning, referred to as multi-party learning (MPL), has become an important technology for utilizing data from multiple parties with pr…
cs.CR2024
Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization
Guopeng Lin, Weili Han, Wenqiang Ruan +4
Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with…