Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

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
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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
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
Konferenzbeitrag, Elektronische Publikation
Fraunhofer AISEC ()
anonymization; Chatter Analysis; edge computing

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.