activity
20202026
most citedLMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors

12 citations · 22 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Ang Li, Ben Liu, Bin Han +215

Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…

cs.CR2024

Ditto: Quantization-aware Secure Inference of Transformers upon MPC

Haoqi Wu, Wenjing Fang, Yancheng Zheng +4

Due to the rising privacy concerns on sensitive client data and trained models like Transformers, secure multi-party computation (MPC) techniques are employed to enable secure infe…

cs.CL2023★ 12 cited

LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors

Chengkun Wei, Wenlong Meng, Zhikun Zhang +6

Prompt-tuning has emerged as an attractive paradigm for deploying large-scale language models due to its strong downstream task performance and efficient multitask serving ability.…

cs.CR2020★ 4 cited

Practical Privacy Preserving POI Recommendation

Chaochao Chen, Jun Zhou, Bingzhe Wu +4

Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the…

cs.LG2020★ 4 cited

Secret Sharing based Secure Regressions with Applications

Chaochao Chen, Liang Li, Wenjing Fang +6

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potent…

cs.LG2020★ 2 cited

Unpack Local Model Interpretation for GBDT

Wenjing Fang, Jun Zhou, Xiaolong Li +1

A gradient boosting decision tree (GBDT), which aggregates a collection of single weak learners (i.e. decision trees), is widely used for data mining tasks. Because GBDT inherits t…