Search Results for author: Wenjing Fang

Found 7 papers, 0 papers with code

Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy

no code implementations18 Aug 2022 Wenqiang Ruan, Mingxin Xu, Wenjing Fang, Li Wang, Lei Wang, Weili Han

Second, to reduce the accuracy loss led by differential privacy noise and the huge communication overhead of MPL, we propose two optimization methods for the training process of MPL: (1) the data-independent feature extraction method, which aims to simplify the trained model structure; (2) the local data-based global model initialization method, which aims to speed up the convergence of the model training.

When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control

no code implementations20 Aug 2020 Chaochao Chen, Jun Zhou, Li Wang, Xibin Wu, Wenjing Fang, Jin Tan, Lei Wang, Alex X. Liu, Hao Wang, Cheng Hong

In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.


Large-Scale Secure XGB for Vertical Federated Learning

no code implementations18 May 2020 Wenjing Fang, Derun Zhao, Jin Tan, Chaochao Chen, Chaofan Yu, Li Wang, Lei Wang, Jun Zhou, Benyu Zhang

Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.

BIG-bench Machine Learning Federated Learning +1

Secret Sharing based Secure Regressions with Applications

no code implementations10 Apr 2020 Chaochao Chen, Liang Li, Wenjing Fang, Jun Zhou, Li Wang, Lei Wang, Shuang Yang, Alex Liu, Hao Wang

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.


Unpack Local Model Interpretation for GBDT

no code implementations3 Apr 2020 Wenjing Fang, Jun Zhou, Xiaolong Li, Kenny Q. Zhu

Besides the commonly used feature importance as a global interpretation, feature contribution is a local measure that reveals the relationship between a specific instance and the related output.

Feature Importance

Towards Non-projective High-Order Dependency Parser

no code implementations COLING 2016 Wenjing Fang, Kenny Zhu, Yizhong Wang, Jia Tan

This paper presents a novel high-order dependency parsing framework that targets non-projective treebanks.

Dependency Parsing

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