S3ML: A Secure Serving System for Machine Learning Inference Article Swipe

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Junming Ma , Chaofan Yu , Aihui Zhou , Bingzhe Wu , Xibin Wu , Xingyu Chen , Xiangqun Chen , Lei Wang , Donggang Cao ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2010.06212

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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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2010.06212
PDF: https://arxiv.org/pdf/2010.06212
OA Status: green
Cited By: 1
References: 28
Related Works: 10
OpenAlex ID: https://openalex.org/W3093179024

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DOI: https://doi.org/10.48550/arxiv.2010.06212

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Title: S3ML: A Secure Serving System for Machine Learning Inference

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-10-13

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Authors: Junming Ma, Chaofan Yu, Aihui Zhou, Bingzhe Wu, Xibin Wu, Xingyu Chen, Xiangqun Chen, Lei Wang, Donggang Cao

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Landing page: https://arxiv.org/abs/2010.06212

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PDF URL: https://arxiv.org/pdf/2010.06212

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2010.06212

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Concepts: Inference, Computer science, Artificial intelligence, Machine learning, Computer security

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Cited by: 1

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Citations by year (recent): 2021: 1

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References (count): 28

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Related works (count): 10

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