基于GPU平台的工业物联网部分同态加密加速研究

Ren-Jun Chong, W. Lee
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引用次数: 2

摘要

工业物联网(IIoT)是一种用于增强制造过程的范式转换技术。在该技术下,云服务器被广泛用于存储通过各个传感器节点采集的传感器数据。存储的数据被用于各种分析和计算,包括机器学习和统计,希望为工业目的找到有用的推论。然而,其中一些传感器数据和分析结果是敏感的,应该妥善处理,以保护用户的隐私。此外,由于潜在的网络安全攻击,云服务器环境可能不完全可信。加密是保护用户隐私的最直接的方法之一,但加密的传感器数据禁止云服务器执行任何进一步的分析。同态加密有助于为传感器数据提供加密,同时允许第三方(云服务器)对加密数据执行计算。然而,同态加密算法通常比较复杂,需要大量的计算量。本文提出了在GPU平台上加速ElGamal部分同态加密的实现技术。此实现允许在高性能的IIoT应用程序的云服务器上执行同态乘法,同时能够保护用户隐私。
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Accelerating ElGamal Partial Homomorphic Encryption with GPU Platform for Industrial Internet of Things
Industrial Internet of Things (IIoT) is a paradigm shifting technology used for enhancing the manufacturing process. Under this technology, cloud server is widely used to store sensor data collected through various sensor nodes. The stored data is being used for various analysis and computation, including machine learning and statistics, in hope of finding useful inferences for industrial purpose. However, some of these sensor data and analysis results are sensitive, which should be handled properly to protect the user privacy. In addition, the cloud server environment may not be fully trusted, due to potential cyber security attack by adversaries. Encryption is one of the most straightforward way to protect user privacy, but encrypted sensor data prohibits the cloud server to perform any further analysis. Homomorphic encryption is useful to provide encryption to sensor data, yet allow the third party (cloud server) to perform computation on the encrypted data. However, homomorphic encryption algorithm is usually complex and require a lot of computational effort. In this paper, we propose implementation technique to accelerate the ElGamal partial homomorphic encryption in GPU platform. This implementation allows homomorphic multiplication to be performed on cloud server for IIoT applications at high performance, yet able to protect the user privacy.
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