Cutting out the trusted third party in business-to-business data exchange: A quantitative study on the impact of multi-party computation on firms’ willingness to share sensitive data in supply chains

Many firms hesitate to share data via a Trusted Third Party (TTP) due to the risk of exposing sensitive information to competitors. Multi-party computation (MPC) is a cryptographic technology that removes the need for TTPs in data sharing, as it allows conducting computations without having to disclose the underlying datasets. While MPC could resolve certain data-sharing barriers, it is uncertain under what conditions business actors prefer MPC over TTP-based data sharing. This paper explores how MPC affects organizational willingness to share sensitive data for collective purposes, focusing on Internet-of-Things (IoT) data within supply chains. We conduct an online experiment on 106 participants representing decision-makers in logistics organizations. We find that participants are more willing to share sensitive data via MPC-based applications than conventional TTP applications. Also, MPC significantly increases participants' perception of trustworthiness and security while sharing sensitive data. However, we find no difference in relative advantage between the two applications. Our study implies that technologies like MPC could allow companies to directly share data without trusted third parties, thus leveling the playing field of the data economy. Hence, regulators and policymakers may consider MPC and related privacy-preserving technologies as a way to break the dominance of big tech corporations in the data economy.