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 |
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Rok vydání: | 2021 |
Předmět: |
0209 industrial biotechnology
Information privacy business.industry Computer science Homomorphic encryption Cryptography Cloud computing 02 engineering and technology Computer security computer.software_genre Encryption Industrial and Manufacturing Engineering Outsourcing 020901 industrial engineering & automation Hardware and Architecture Control and Systems Engineering Analytics 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing business computer Software |
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 |
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