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64 SMT007 MAGAZINE I DECEMBER 2022 In my case, the privacy restrictions I set on my data prevented AIs from accessing certain facts about my purchase—which I'm very happy about. Advertising companies who own these AIs will argue that more data leads to better value from their services, but is that worth the potential loss of our privacy? is conundrum is a massive issue facing almo st all digital transformation, Smar t manufacturing, and Industry 4.0 projects. e situation is not so bad within a factory running, for example, a Smart MES solution. Data related to design, manufacturing capabilities and configurations, operations, measurements, etc., are all collected and stored in either a secure manufacturing environment or private cloud storage space. An MES can produce a variety ofvalues by contextualizing the collected data, providing visibility, control, assurance, and optimization of manufacturing execution through material flows, operator assignments, and both machine and operator work instructions. Well, at least that used to be the case. is open sharing vs. privacy issue can now be seen in Smart manufacturing, where external parties might request—even demand—access to private manufacturing information. Data relating to customer assurance, proof of product and material provenance, predictive m a i n te n a n c e , e n e r g y p r o f i l i ng , q u a l i t y analytics—everything is fair game. Honoring these requests means potentially exposing sensitive intellectual property details. Further, intelligence—whether artificial or human— can use this newly shared information for other pur po ses. How do we determine what data to share, who to share it with, and what purposes it can be used for? Once we decide these parameters, how do we enforce them? e good news is that new digital technology standards, called Distributed IDs (DIDs) and Ver if iable Credentials ( VCs), have been developed by the World-Wide Web Consortium (W3C) to bring a simple resolution to this conundrum. ese technologies use the blockchain to allow provable facts to be shared between authorized parties without the disclosure of the original private information. IPC has already begun work on new standards to determine how these technologies may be utilized within or around the IPC Digital Twin, traceability, cybersecurity, component- level assurance, Connected Factory Exchange (CFX), and IPC-2581 design data standards. What might this look like in practice? A VC, for example, could help determine whether a product can carry a "Made in the USA" designation. is is by no means a simple question, as a certain percentage of assembly and material sourcing locations need to be considered before making the determination. All that data is recorded, with bits and pieces retained by the many manufacturing, assembly, and distribution companies involved in the supply network. e VC is a trusted algorithm that both calculates the proof of a claim and provides this claim as a small digital identity. Together with the proven identity (DID) of the source, this identity is stored openly (but securely) using blockchain technology so that companies can avoid sharing protected information while preventing any data tampering. roughout the supply network, MES solutions associate mater ials w ith products at the point of assembly and thereby

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