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cs.LG2024★ 1 cited
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference
Xiangrui Xu, Qiao Zhang, Rui Ning +2
The prevalent use of Transformer-like models, exemplified by ChatGPT in modern language processing applications, underscores the critical need for enabling private inference essent…
cs.LG2019★ 2 cited
CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations
Qiao Zhang, Cong Wang, Chunsheng Xin +1
Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on end devices. However, such convenience comes with a cost of privacy because users have to up…