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Edge-computing enhanced privacy protection for industrial ecosystems in the context of SMEs

: Giehl, Alexander; Schneider, Peter; Busch, Maximilian; Schnoes, Florian; Kleinwort, Robin; Zaeh, Michael F.

Postprint urn:nbn:de:0011-n-6154587 (1.4 MByte PDF)
MD5 Fingerprint: b37e33992de17019eccd5cf341dc7c5d
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Erstellt am: 26.11.2020

Institute of Electrical and Electronics Engineers -IEEE-:
12th CMI Conference on Cybersecurity and Privacy, CMI 2019 : Copenhagen, Denmark, 28-29 November 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-2856-6
ISBN: 978-1-7281-2857-3
Conference on Cybersecurity and Privacy (CMI) <12, 2019, Copenhagen>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
20449 N; Anonymization4Optimization (A4O)
Konferenzbeitrag, Elektronische Publikation
Fraunhofer AISEC ()
Edge-Computing; privacy; Industrie 4.0; Chatter Detection; anomaly detection; Small- and Medium-Sized Companies; security

The ongoing transformation of the manufacturing landscape introduces new business opportunities for enterprises but also brings new challenges with it. Especially small- and medium-sized companies (SMEs) require an increasing effort to stay competitive. Data produced on the shop-floor can be harnessed to conduct analyses useful to plant operators, e.g., for optimization of production capabilities or for increasing plant security. Therefore, we propose a privacy-preserving edge computing architecture to facilitate a platform for utilizing such applications. Our approach is motivated by requirements from SMEs in Germany, e.g., protection of intellectual property, and employs suitable privacy models. We demonstrate the viability of the proposed framework by evaluation of uses cases for machine chatter optimization and anomaly detection within plants.