Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/238001 
Year of Publication: 
2021
Series/Report no.: 
23rd Biennial Conference of the International Telecommunications Society (ITS): "Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world", Online Conference / Gothenburg, Sweden, 21st-23rd June, 2021
Publisher: 
International Telecommunications Society (ITS), Calgary
Abstract: 
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.
Subjects: 
multi-party computation
data sharing
willingness to share sensitive data
data economy
Document Type: 
Conference Paper

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