Secure Computation in Decentralized Data Markets
This addresses privacy concerns for data contributors in decentralized markets, but it is incremental as it applies existing cryptographic methods to a specific domain.
The paper tackled the problem of performing secure computation on decentralized data markets to gain insights while preserving contributor privacy, by designing efficient protocols using secure multi-party computation techniques like garbled circuits and homomorphic encryption, with performance demonstrated on healthcare applications.
Decentralized data markets gather data from many contributors to create a joint data cooperative governed by market stakeholders. The ability to perform secure computation on decentralized data markets would allow for useful insights to be gained while respecting the privacy of data contributors. In this paper, we design secure protocols for such computation by utilizing secure multi-party computation techniques including garbled circuit evaluation and homomorphic encryption. Our proposed solutions are efficient and capable of performing arbitrary computation, but we report performance on two specific applications in the healthcare domain to emphasize the applicability of our methods to sensitive datasets.