Julen Bernabé-Rodríguez, Albert Garreta, Oscar Lage
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引用次数: 0
Abstract
Big data has proven to be a very useful tool for companies and users, but companies with larger datasets have ended being more competitive than the others thanks to machine learning or artificial inteligence. Secure multi-party computation (SMPC) allows the smaller companies to jointly train arbitrary models on their private data while assuring privacy, and thus gives data owners the ability to perform what are currently known as federated learning algorithms. Besides, with a blockchain it is possible to coordinate and audit those computations in a decentralized way. In this document, we consider a private data marketplace as a space where researchers and data owners meet to agree the use of private data for statistics or more complex model trainings. This document presents a candidate architecure for a private data marketplace by combining SMPC and a public, general-purpose blockchain. Such a marketplace is proposed as a smart contract deployed in the blockchain, while the privacy preserving computation is held by SMPC.
期刊介绍：
ACM Transactions on Privacy and Security (TOPS) (formerly known as TISSEC) publishes high-quality research results in the fields of information and system security and privacy. Studies addressing all aspects of these fields are welcomed, ranging from technologies, to systems and applications, to the crafting of policies.
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