52 citations · 58 across the 5 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 1 cited
S3ML: A Secure Serving System for Machine Learning Inference
Junming Ma, Chaofan Yu, Aihui Zhou +6
We present S3ML, a secure serving system for machine learning inference in this paper. S3ML runs machine learning models in Intel SGX enclaves to protect users' privacy. S3ML desig…
cs.CR2020★ 1 cited
Secure Collaborative Training and Inference for XGBoost
Andrew Law, Chester Leung, Rishabh Poddar +6
In recent years, gradient boosted decision tree learning has proven to be an effective method of training robust models. Moreover, collaborative learning among multiple parties has…
cs.LG2020★ 52 cited
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…