Homomorphic Encryption as a secure PHM outsourcing solution for small and medium manufacturing enterprise

Autor: Ha Eun David Kang, Brian W. Anthony, Sangwoon Kim, David Donghyun Kim, Jung Hee Cheon, Duhyeong Kim
Rok vydání: 2021
Předmět:
Zdroj: Journal of Manufacturing Systems. 61:856-865
ISSN: 0278-6125
DOI: 10.1016/j.jmsy.2021.06.001
Popis: Small and medium manufacturing enterprises (SMEs) often lack skills and resources required to perform in-house PHM analytics. While cloud-based services provide SMEs the option to outsource PHM analytics in the cloud, a critical limiting factor to such arrangement is the data owner’s unwillingness to share data due to data privacy concerns. In this paper, we showcase how homomorphic encryption, a cryptographic technique that allows direct computation on encrypted data, can enable a secure PHM outsourcing with high precision for SMEs. We first outline a two-party collaborative framework for a secure outsourcing of PHM analytics for SMEs. Next, we introduce a frequency-based peak detection algorithm (H-FFT-C) that generates a machine health diagnosis and prescription report, while keeping the machine data private. We demonstrate the secure PHM outsourcing scenario on a lab-scale fiber extrusion device. Our demonstration is comprised of key functionalities found in many PHM applications. Finally, the extensibility and limitation of the approach used in this study is summarized.
Databáze: OpenAIRE