TAPAS：加速（加密）预测即服务的技巧
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
摘要：
机器学习方法被广泛用于各种预测问题。\emph{Prediction as a service}是具有技术专长和计算资源的服务提供商可以为客户端执行预测的范例。然而，数据隐私严重限制了这些服务的适用性，除非设计了使客户数据保密的措施（甚至对服务提供商）。同样重要的是尽量减少客户端和服务器之间所需的计算和通信量。全同态加密提供了一种可能的出路，客户端可以借此加密其数据，服务器可以在其上执行算术计算。使用全同态加密的主要缺点是在加密数据上评估大型机器学习模型所需的时间量。我们结合了机器学习文献中的思想，特别是神经网络的二值化和稀疏化，以及使用加密数据加速和并行计算的算法工具。
英文摘要：
Machine learning methods are widely used for a variety of prediction problems. \emph{Prediction as a service} is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services, unless measures to keep client data private (even from the service provider) are designed. Equally important is to minimize the amount of computation and communication required between client and server. Fully homomorphic encryption offers a possible way out, whereby clients may encrypt their data, and on which the server may perform arithmetic computations. The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data. We combine ideas from the machine learning literature, particularly work on binarization and sparsification of neural networks, together with algorithmic tools to speed-up and parallelize computation using encrypted data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
