S3ML: A Secure Serving System for Machine Learning Inference
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 designs a secure key management service to construct flexible privacy-preserving server clusters and proposes novel SGX-...
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| Veröffentlicht in: | arXiv.org 2020-10 |
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| creator | Ma, Junming Yu, Chaofan Zhou, Aihui Wu, Bingzhe Wu, Xibin Chen, Xingyu Chen, Xiangqun Wang, Lei Cao, Donggang |
| description | 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 designs a secure key management service to construct flexible privacy-preserving server clusters and proposes novel SGX-aware load balancing and scaling methods to satisfy users' Service-Level Objectives. We have implemented S3ML based on Kubernetes as a low-overhead, high-available, and scalable system. We demonstrate the system performance and effectiveness of S3ML through extensive experiments on a series of widely-used models. |
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| subjects | Inference Machine learning Privacy Service introduction User satisfaction |
| title | S3ML: A Secure Serving System for Machine Learning Inference |
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