A Decentralized Private Data Marketplace using Blockchain and Secure Multi-Party Computation

被引：3
|
作者：
Bernabe-Rodriguez, Julen [1 ]
Garreta, Albert [2 ]
Lage, Oscar [1 ]
机构：
[1] TECNALIA, Basque Res & Technol Alliance BRTA, Bizkaia Sci & Technol Pk,Bldg 700, E-48160 Derio, Bizkaia, Spain
[2] Basque Ctr Appl Math, Alameda Mazarredo 14, E-48009 Bilbao, Bizkaia, Spain
来源：
ACM TRANSACTIONS ON PRIVACY AND SECURITY | 2024年 / 27卷 / 02期
关键词：
Multi-party computation; blockchain; edge computing; distributed computation; data economy;
D O I：
10.1145/3652162
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 intelligence. 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.
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页数：29
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