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Leveraging Edge Computing and Differential Privacy to Securely Enable Industrial Cloud Collaboration Along the Value Chain

 
: Giehl, Alexander; Heinl, Michael P.; Busch, Maximilian

:
Postprint urn:nbn:de:0011-n-6410767 (814 KByte PDF)
MD5 Fingerprint: 37f76943575cc93768588d091f093116
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Erstellt am: 19.10.2021


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 17th International Conference on Automation Science and Engineering, CASE 2021 : August 23-27, 2021, Lyon, France
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-6654-4809-3
ISBN: 978-1-6654-1872-0
ISBN: 978-1-6654-1873-7
S.2023-2028
International Conference on Automation Science and Engineering (CASE) <17, 2021, Lyon>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
20449 N; Anonymization4Optimization (A4O)
Anonymisierung von Prozessdaten zur Optimierung von Werkzeugmaschinen unter Verwendung von Cloud-Services
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer AISEC ()
anonymization; Chatter Analysis; edge computing

Abstract
Big data continues to grow in the manufacturing domain due to increasing interconnectivity on the shop floor in the course of the fourth industrial revolution. The optimization of machines based on either real-time or historical machine data provides benefits to both machine producers and operators. In order to be able to make use of these opportunities, it is necessary to access the machine data, which can include sensitive information such as intellectual property. Employing the use case of machine tools, this paper presents a solution enabling industrial data sharing and cloud collaboration while protecting sensitive information. It employs the edge computing paradigm to apply differential privacy to machine data in order to protect sensitive information and simultaneously allow machine producers to perform the necessary calculations and analyses using this data.

: http://publica.fraunhofer.de/dokumente/N-641076.html