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Architecture for Privacy-Preserving Greenhouse Gas Emissions Calculation Among Multiple Organizations Using Homomorphic Encryption
To curb global warming, the calculation of the amount of greenhouse gas (GHG) emissions must be discussed. The GHG protocol, an international GHG accounting standard, requires the calculation of indirect GHG emissions, called Scope 3 GHG emissions, throughout the supply chain. Accurate calculation of GHG emissions requires primary data, such as sensor data. However, because Scope 3 GHG emissions cross multiple organizations, the calculation of indirect emissions has some problems from the perspectives of trade secrecy, cost of aggregation, and data integrity. In this study, we propose an architecture to solve those problems using homomorphic encryption. Trade secrets are protected because GHG emissions are calculated in encrypted form. Aggregation is simplified because the calculated data are stored in the database organized for information extraction. If there is a problem with the data, it can be traced back. To evaluate the validity of the proposed architecture, we performed a case study focusing on GHG emissions from vehicle travel, which is part of a real supply chain. We experimentally evaluated the computation time at the edge and the data capacity to be communicated on the local 5G network. Consequently, our proposed architecture worked well and could be used to calculate Scope 3 GHG emissions.
Architecture for Privacy-Preserving Greenhouse Gas Emissions Calculation Among Multiple Organizations Using Homomorphic Encryption
To curb global warming, the calculation of the amount of greenhouse gas (GHG) emissions must be discussed. The GHG protocol, an international GHG accounting standard, requires the calculation of indirect GHG emissions, called Scope 3 GHG emissions, throughout the supply chain. Accurate calculation of GHG emissions requires primary data, such as sensor data. However, because Scope 3 GHG emissions cross multiple organizations, the calculation of indirect emissions has some problems from the perspectives of trade secrecy, cost of aggregation, and data integrity. In this study, we propose an architecture to solve those problems using homomorphic encryption. Trade secrets are protected because GHG emissions are calculated in encrypted form. Aggregation is simplified because the calculated data are stored in the database organized for information extraction. If there is a problem with the data, it can be traced back. To evaluate the validity of the proposed architecture, we performed a case study focusing on GHG emissions from vehicle travel, which is part of a real supply chain. We experimentally evaluated the computation time at the edge and the data capacity to be communicated on the local 5G network. Consequently, our proposed architecture worked well and could be used to calculate Scope 3 GHG emissions.
Architecture for Privacy-Preserving Greenhouse Gas Emissions Calculation Among Multiple Organizations Using Homomorphic Encryption
Sasaki, Yusuke (author) / Segawa, Tomoka (author) / Michikata, Takashi (author) / Koshizuka, Noboru (author)
2023-09-24
3173195 byte
Conference paper
Electronic Resource
English
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