52 citations · 86 across the 10 of their papers we have counts for
10 papers
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,…
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…
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.…
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy
Wenqiang Ruan, Mingxin Xu, Wenjing Fang +3
Secure multi-party computation-based machine learning, referred to as MPL, has become an important technology to utilize data from multiple parties with privacy preservation. While…
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
Chaochao Chen, Jun Zhou, Li Wang +7
Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and t…
Large-Scale Secure XGB for Vertical Federated Learning
Wenjing Fang, Derun Zhao, Jin Tan +6
Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force. Under such situation, Federated Learnin…
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…
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…
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…
Adapted tree boosting for Transfer Learning
Wenjing Fang, Chaochao Chen, Bowen Song +3
Secure online transaction is an essential task for e-commerce platforms. Alipay, one of the world's leading cashless payment platform, provides the payment service to both merchant…