论文标题
部分可观测时空混沌系统的无模型预测
A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service
论文作者
论文摘要
深度学习 - 即服务是一种新颖而有希望的计算范式，旨在通过基于云的计算基础架构提供机器/深度学习解决方案和机制。由于其能够远程执行和训练深度学习模型（通常需要高计算负载和内存职业），这种方法可以确保高性能，可扩展性和可用性。 Unfortunately, such an approach requires to send information to be processed (e.g., signals, images, positions, sounds, videos) to the Cloud, hence having potentially catastrophic-impacts on the privacy of users.本文介绍了一种新颖的分布式体系结构，用于深入学习，即服务，该体系结构能够保留用户敏感的数据，同时提供基于云的机器和深度学习服务。拟议的体系结构依赖于能够在加密数据上执行操作的同态加密，已针对图像分析域中的卷积神经网络（CNN）量身定制，并通过基于客户端服务器静止的方法实现。实验结果表明了所提出的体系结构的有效性。
Deep-learning-as-a-service is a novel and promising computing paradigm aiming at providing machine/deep learning solutions and mechanisms through Cloud-based computing infrastructures. Thanks to its ability to remotely execute and train deep learning models (that typically require high computational loads and memory occupation), such an approach guarantees high performance, scalability, and availability. Unfortunately, such an approach requires to send information to be processed (e.g., signals, images, positions, sounds, videos) to the Cloud, hence having potentially catastrophic-impacts on the privacy of users. This paper introduces a novel distributed architecture for deep-learning-as-a-service that is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services. The proposed architecture, which relies on Homomorphic Encryption that is able to perform operations on encrypted data, has been tailored for Convolutional Neural Networks (CNNs) in the domain of image analysis and implemented through a client-server REST-based approach. Experimental results show the effectiveness of the proposed architecture.
