dc.contributor.author | Brucker, AD | |
dc.contributor.author | Yalman, S | |
dc.date.accessioned | 2021-10-18T10:05:34Z | |
dc.date.issued | 2022-01-01 | |
dc.description.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. | en_GB |
dc.description.sponsorship | Turkish Ministry of National Education | en_GB |
dc.identifier.citation | Vol. 436, pp. 434-446| | en_GB |
dc.identifier.doi | 10.1007/978-3-030-94343-1_33 | |
dc.identifier.uri | http://hdl.handle.net/10871/127492 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer Verlag | en_GB |
dc.relation.url | https://git.logicalhacking.com/PrivacyPreservingLCA/ConfidentialLCA | en_GB |
dc.rights.embargoreason | Under embargo until 1 January 2023 in compliance with publisher policy | en_GB |
dc.rights | © Springer Nature Switzerland AG 2022 | |
dc.subject | Life-Cycle Assessment | en_GB |
dc.subject | LCA | en_GB |
dc.subject | Confidential Computation | en_GB |
dc.subject | Secure Multi-Party Computation | en_GB |
dc.subject | SMPC | en_GB |
dc.title | Confidentiality Enhanced Life-Cycle Assessment | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2021-10-18T10:05:34Z | |
dc.contributor.editor | Marrella, A | en_GB |
dc.contributor.editor | Weber, B | en_GB |
dc.identifier.issn | 1865-1348 | |
exeter.place-of-publication | Heidelberg | en_GB |
dc.description | This is the author accepted manuscript. The final version is available from Springer via the DOI in this record | en_GB |
dc.description | Availability. Our prototype is available at https://git.logicalhacking.
com/PrivacyPreservingLCA/ConfidentialLCA under an Apache license
(SPDX-License-Identifier: Apache-2.0). | en_GB |
dc.description | Business Process Management Workshops: BPM 2021 International Workshops, Rome, Italy, 6–10 September 2021 | en_GB |
dc.identifier.journal | Lecture Notes in Business Information Processing | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2021-10-18 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2021-10-18T10:03:36Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2023-01-01T00:00:00Z | |
refterms.panel | B | en_GB |