It is not (only) about privacy: How multi-party computation redefines control, trust, and risk in data sharing

Abstract

Firms are often reluctant to share data because of mistrust, concerns over control, and other risks. Multi-party computation (MPC) is a new technique to compute meaningful insights without having to transfer data. This paper investigates if MPC affects known antecedents for data sharing decisions: control, trust, and risks. Through 23 qualitative interviews in the automotive industry, we find that MPC (1) enables new ways of technology-based control, (2) reduces the need for inter-organizational trust, and (3) prevents losing competitive advantage due to data leakage. However, MPC also creates the need to trust technology and introduces new risks of data misuse. These impacts arise if firms perceive benefits from sharing data, have high organizational readiness, and perceive data as non-sensitive. Our findings show that known antecedents of data sharing should be specified differently with MPC in place. Furthermore, we suggest reframing MPC as a data collaboration technology beyond enhancing privacy.

The Perceived Influence Model of Trust: Toward a Multi-Trustee Theory

“…A different study by Agahari et al (2022) examined how trust in “multi-party computation” processes reduced the need for organizations to trust one another when sharing data. They found that trust in the computation processes can compensate for low/uncertain trust between organizations to facilitate data sharing.…”

“…Type and amount of influence also have not been extensively considered in trust theory and research. Most research assumes the trustor is giving up some control, thereby allowing one or more trustees to have some influence, without detailing the control/influence characteristics (see Agahari et al, 2022, for an exception). Beyond concepts such as collaboration, coordination, and competition, prior work has conceptualized variations in control in terms of formal and informal mechanisms focused on inputs, outputs, and processes (e.g., Wiener et al, 2016).…”

Toward Business Models for a Meta-Platform: Exploring Value Creation in the Case of Data Marketplaces

“…In line with this finding, van de Ven et al ( 2021) also stress the importance of secure data sharing, high (and unique) data assets, and easy data tooling. Concerning security and privacy concerns, for example, data marketplaces frequently employ emerging technologies (such as multi-party computation) to improve trust and reduce risk in data sharing (Agahari et al, 2022) Spiekermann (2019 suggests that data marketplaces should go beyond sharing "raw" data. Instead, they need to provide analytical functionality.…”

Electronic Markets on platform culture

“…To determine the impact of MPC on the perceived control and risks as well as on trust in the context of sharing data among businesses in the automotive industry, the authors Wirawan Agahari, Hosea Ofe and Mark de Reuver conduct a qualitative study with 23 interviews. Their conclusions are ambivalent in the sense that MPC positively influences control, risks and trust, but requires new forms of trust in technology and demands the measures to contain risks regarding the misuse of data (Agahari et al, 2022). For data sharing organizations, the authors call for data-driven mindsets and capabilities, which may be seen as an element account services are known to be commodity banking services, which differ little among competitors and are rather sensitive to cost.…”