In a recent study by the Christian Doppler Laboratory for Sustainable Product Management, co-funded by iPoint Systems, different digital methods were explored for preserving confidentiality in data exchange along value chains. The study focused on cryptographic and statistical approaches and the potential of Probabilistic Machine Learning in the context of data from sustainable product management and digital product passports.
To enable sustainable product management (SPM), product life cycle data play a significant role. Such data are, for instance, the pre-requisite to conduct Life Cycle Assessments and are further handled as the critical component of Digital Product Passports (DPPs). Such passports can be valuable SPM decision support tools, as they may function as product life cycle data carriers. Consequently, a DPP could provide valuable insights into a product's lifecycle, enabling businesses to conduct more sustainable and circular operations. However, product life cycle data are often characterized by sensitivity, resulting in stakeholders’ reluctance to share crucial data. This poses significant challenges to the full potential of DPPs in supporting SPM. Therefore, confidentiality-preserving data exchange is of importance in this context.
To address the issue of data sensitivity, several cryptographic and machine-learning approaches can be employed to ensure confidentiality-preserving data exchange. These approaches can be broadly categorized into cryptographic and statistical methods.
Cryptographic approaches such as homomorphic encryption, multiparty computation, and zero-knowledge proofs provide cryptographic guarantees of confidentiality. They ensure that sensitive information is only accessible to designated parties, encouraging stakeholders to share crucial data without fear of being misused.
On the other hand, statistical approaches like federated learning, differential privacy, and invariant/fair learning provide statistical guarantees of confidentiality. They ensure that confidentiality holds with a specified probability, even when dealing with large amounts of data. These methods can be used to obtain data or models from which statistical relationships have been removed, thus preserving the privacy of sensitive information.
It needs to be noted that the deployment of either cryptographic or statistical approaches depends on the SPM use case at hand.
As shown in Figure 1, the paper introduces a concept of model sharing for confidentiality-preserving data exchange via a DPP. The specific use case concerns sharing Electric Vehicle Battery (EVB) in-use data. Such data is required to enable Battery Second Use (B2U) business models while being attributed with high sensitivity by the Original Equipment Manufacturer (OEM). The proposed concept illustrates EVB in-use data sharing between an OEM and a third-party actor that pursues a B2U business model based on two scenarios:
The choice between the two scenarios would depend on the level of confidentiality required and the trust relationship between the OEM and other stakeholders.
Figure 1: Two potential scenarios of how model sharing could be deployed for confidentiality-preserving data exchange via a digital product passport (DPP).
In conclusion, deploying data science and machine learning approaches for confidentiality-preserving data exchange holds promise for SPM. Thus, further research of the CD-Laboratory and iPoint -systems is focused on demonstrating the potential in other use cases in the context of DPPs and Life Cycle Assessment.
This study was published in Procedia CIRP and the full paper „Confidentiality-preserving data exchange to enable sustainable product management via digital product passports – a conceptualization” by Katharina Berger, Magdalena Rusch, Antonia Pohlmann, Martin Popowicz, Bernhard C. Geiger, Heimo Gursch, Josef-Peter Schöggl and Rupert J. Baumgartner can be read here: https://doi.org/10.1016/j.procir.2023.02.060.