# Secure Computation in Decentralized Data Markets

## TL;DR

This paper presents efficient secure computation protocols for decentralized data markets, enabling privacy-preserving data analysis on sensitive datasets using secure multi-party computation techniques.

## Contribution

It introduces novel secure protocols utilizing garbled circuits and homomorphic encryption tailored for decentralized data markets, demonstrating their applicability in healthcare.

## Key findings

- Protocols support arbitrary computation
- Efficient performance on healthcare datasets
- Applicable to privacy-sensitive data analysis

## Abstract

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.