Privacy-Preserving Production Process Parameter Exchange

Pennekamp, Jan; Buchholz, Erik; Lockner, Yannik; Dahlmanns, Markus; Xi, Tiandong; Fey, Marcel; Brecher, Christian; Hopmann, Christian; Wehrle, Klaus

doi:10.1145/3427228.3427248

Cited by 17 publications

(10 citation statements)

References 70 publications

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“…Regarding the sharing of production parameters and associated experiences, we analyzed the industry needs when commissioning new production lines and correspondingly developed an oblivious exchange platform to facilitate such information sharing [119]. Again, we rely on well-known concepts from confidential computing to ensure security and real-world deployability in the WWL.…”

Section: World Wide Lab (Wwl)mentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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Section: World Wide Lab (Wwl)mentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

Self Cite

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“…However, this approach is limited to transferring finished digital products and does not consider the exchange of manufacturing information. Researchers in [23] combine Bloom filters and Oblivious Transfer to guarantee a private industrial parameters exchange. However, this solution reduces the control retained by data producers by allowing customers to retrieve data without the data producers knowing what has been transferred.…”

Section: State Of the Artmentioning

confidence: 99%

“…The trade of physical products generates a multitude of sensitive data, which, if not protected, could be used by attackers and competitors. Pennekamp et al [23] Epiphaniou et al [7] Pennekamp et al [21] Dahlmanns et al [6] Qi et al [24] Pennekamp et al [22] R2. Working with limited computational power.…”

Section: Design Requirementsmentioning

confidence: 99%

End to End Secure Data Exchange in Value Chains with Dynamic Policy Updates

Mosteiro-Sanchez¹,

Barceló²,

Astorga³

et al. 2022

Preprint

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Data exchange among value chain partners provides them with a competitive advantage, but the risk of exposing sensitive data is ever-increasing. Information must be protected in storage and transmission to reduce this risk, so only the data producer and the final consumer can access or modify it. End-to-end (E2E) security mechanisms address this challenge, protecting companies from data breaches resulting from value chain attacks. Moreover, value chain particularities must also be considered. Multiple entities are involved in dynamic environments like these, both in data generation and consumption. Hence, a flexible generation of access policies is required to ensure that they can be updated whenever needed. This paper presents a CP-ABE-reliant data exchange system for value chains with E2E security. It considers the most relevant security and industrial requirements for value chains. The proposed solution can protect data according to access policies and update those policies without breaking E2E security or overloading field devices. In most cases, field devices are IIoT devices, limited in terms of processing and memory capabilities. The experimental evaluation has shown the proposed solution's feasibility for IIoT platforms.

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“…Secure industrial collaborations across supply chains (Ö), e.g., to reduce ramp-up costs [10]. To cover P1 and P2 not only with today's (established) long-term trust but also in settings with dynamically evolving and flexible short-term relationships, we identify a new research pillar (P3)…”

Section: (P0)mentioning

confidence: 99%

The Road to Accountable and Dependable Manufacturing

et al. 2021

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The Internet of Things provides manufacturing with rich data for increased automation. Beyond company-internal data exploitation, the sharing of product and manufacturing process data along and across supply chains enables more efficient production flows and product lifecycle management. Even more, data-based automation facilitates short-lived ad hoc collaborations, realizing highly dynamic business relationships for sustainable exploitation of production resources and capacities. However, the sharing and use of business data across manufacturers and with end customers add requirements on data accountability, verifiability, and reliability and needs to consider security and privacy demands. While research has already identified blockchain technology as a key technology to address these challenges, current solutions mainly evolve around logistics or focus on established business relationships instead of automated but highly dynamic collaborations that cannot draw upon long-term trust relationships. We identify three open research areas on the road to such a truly accountable and dependable manufacturing enabled by blockchain technology: blockchain-inherent challenges, scenario-driven challenges, and socio-economic challenges. Especially tackling the scenario-driven challenges, we discuss requirements and options for realizing a blockchain-based trustworthy information store and outline its use for automation to achieve a reliable sharing of product information, efficient and dependable collaboration, and dynamic distributed markets without requiring established long-term trust.

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Privacy-Preserving Production Process Parameter Exchange

Cited by 17 publications

References 70 publications

A Computer Science Perspective on Digital Transformation in Production

A Computer Science Perspective on Digital Transformation in Production

End to End Secure Data Exchange in Value Chains with Dynamic Policy Updates

The Road to Accountable and Dependable Manufacturing

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