Performance Evaluation of Privacy-Preserving Life Cycle Assessment Using Secure Multi-Party Computation Protocols
Year: 2025
Type: conference-paper
Abstract: The growing need for compliance with sustainability regulations force the adoption of sustainable practices, which consequently establish the need for data sharing for the purpose of Life Cycle Assessment (LCA), a critical method for evaluating environmental impacts. One key area where these data are essential is in calculating Scope 3 emissions, which can account for up to 90% of an organization’s total carbon footprint and require input from its suppliers or service providers. However, the confidential nature of industrial process data poses a hindrance to data sharing throughout the supply chain. To address this issue, this study explores the use of secure multiparty computation (SMPC) to enable privacy-preserving LCA computations. The performance of various SMPC protocols is evaluated in different security models (dishonest and honest majority, malicious, covert, and semi-honest) and computational domains (arithmetic modulo prime and modulo 2k). The evaluation focuses on key performance factors, such as computation time, data exchange, and communication rounds, using a flat supply chain model. The findings highlight the trade-offs between security, performance, and scalability for different SMPC protocols in the context of LCA. The study demonstrates the feasibility of applying arithmetic circuit-based SMPC for LCA over other protocols.
Institution: Aalto University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415969767