Multi-Party Computation as a Privacy-Enhancing Technology: Implications for Data Sharing by Businesses and Consumers

Data sharing through data marketplaces, which rely on a Trusted Third Party (TTP), can benefit businesses and society. However, many companies and consumers are increasingly reluctant to share data due to mounting concerns over data control and privacy. Emerging privacy-enhancing technologies (PETs) like Multi-Party Computation (MPC), which...

Rethinking consumers' data sharing decisions with the emergence of multi-party computation: An experimental design for evaluation

Consumers are increasingly reluctant to share their personal data with businesses due to mounting concerns over privacy and control. Emerging privacy-enhancing technologies like multi-party computation (MPC), which allows generating insights while consumers retain data control, are challenging the current understanding of why consumers share...

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

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...

Business model implications of privacy-preserving technologies in data marketplaces: The case of multi-party computation

Privacy-preserving technologies could allow data marketplaces to deliver technical assurances to companies on data privacy and control. However, how such technologies change the business model of data marketplaces is not fully understood. This paper aims to bridge this gap by focusing on multi-party computation (MPC) as a cryptographic...