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Yueying Li et al
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STAMP: Lightweight TEE-Assisted MPC for Efficient Privacy-Preserving Machine Learning
Our paper introduces STAMP, an end-to-end 3-party MPC protocol for efficient privacy-preserving machine learning inference. USTAMP combines MPC protocol with a lightweight TEE (LTEE) to reduce MPC overhead while avoiding challenges in a traditional TEE. STAMP achieves significantly lower inference overhead than state-of-the-art MPC protocols with either CPU or GPU, under either a WAN or LAN setting.
Pengzhi Huang
,
Thang Hoang
,
Yueying Li
,
Elaine Shi
,
G. Edward Suh
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