Business model implications of privacy-preserving technologies in data marketplaces

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

Agahari, W. (TU Delft Information and Communication Technology)
Dolci, R. (Student TU Delft)
de Reuver, G.A. (TU Delft Information and Communication Technology)

2021

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 technology that is currently being hyped. Based on interviews with privacy and security experts, we find that MPC enables data marketplaces to employ a “privacy-as-a-service” business model, which goes beyond privacy-preserving data exchange. Depending on the architecture, MPC could transform data marketplaces into data brokers or data aggregators. More complex architectures might lead to more robust security guarantees and lower trust requirements towards data marketplace operators. Furthermore, MPC enables new offerings of privacy-preserving analytics and services as new revenue sources. Our findings contribute to developing business models of privacy-preserving data marketplaces to unlock the potential of data sharing in a digitized economy.

data marketplaces
multi-party computation
privacy-as-a-service
business model

http://resolver.tudelft.nl/uuid:570b551c-c101-4493-b256-c778c6b32128

Association of the Information Systems (AIS)

29th European Conference on Information Systems (ECIS 2021): Human Values Crisis in a Digitizing World

29th European Conference on Information Systems (ECIS 2021) A Virtual AIS Conference, 2021-06-14 → 2021-06-16, Morocco

Institutional Repository

conference paper

© 2021 W. Agahari, R. Dolci, G.A. de Reuver