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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 edgecomputing 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.

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