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Confidentiality Enhanced Life-Cycle Assessment

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Business Process Management Workshops (BPM 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 436))

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Abstract

The environmental impact of products is an important factor in buying decisions of customers and it is also an increasing concern of law makers. Hence, companies are interested in determining the ecological footprint of their products. Life-cycle assessment (LCA) is a standardized method for computing the ecological footprint of a product.

Today, LCA is usually not computed in real-time and neither is LCA using actual sensor data: in contrast it is computed “offline” using “historic” values based on exemplary measurements. With the rise of the Internet of Things (IoT), LCA computations can be based on actual production processes. While an LCA based on actual sensor data is desirable from an ecological perspective, it also can reveal trade secrets, e.g., details about production processes or business relationships.

In this paper, we present an approach, using secure multi-party computation, to enable the confidential data sharing required for an LCA computation using sensor data.

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Acknowledgements

This work was partially funded by the Turkish Ministry of National Education.

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Authors and Affiliations

  1. Department of Computer Science, University of Exeter, Exeter, UK

    Achim D. Brucker & Sakine Yalman

Authors
  1. Achim D. Brucker
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  2. Sakine Yalman
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Correspondence to Sakine Yalman .

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Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Andrea Marrella

  2. University of St. Gallen, St. Gallen, Switzerland

    Barbara Weber

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Our prototype is available at https://git.logicalhacking.com/PrivacyPreservingLCA/ConfidentialLCA under an Apache license (SPDX-License-Identifier: Apache-2.0).

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Brucker, A.D., Yalman, S. (2022). Confidentiality Enhanced Life-Cycle Assessment. In: Marrella, A., Weber, B. (eds) Business Process Management Workshops. BPM 2021. Lecture Notes in Business Information Processing, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-030-94343-1_33

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  • DOI: https://doi.org/10.1007/978-3-030-94343-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94342-4

  • Online ISBN: 978-3-030-94343-1

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