Performance Evaluation of Privacy-Preserving Life Cycle Assessment Using Secure Multi-Party Computation Protocols

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 2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> ). 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.