S3ML: A Secure Serving System for Machine Learning Inference

Authors: Junming Ma, Chaofan Yu, Aihui Zhou, Bingzhe Wu, Xibin Wu, Xingyu Chen, Xiangqun Chen, Lei Wang, Donggang Cao
Published: 2020-10-13

Source: https://arxiv.org/abs/2010.06212

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

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SGXアプリケーション開発 機械学習 情報セキュリティ

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Abstract

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.