Self-Sovereign and Secure Data Sharing Through Docker Containers for Machine Learning on Remote Node

Abstract

Collecting personal data from various sources and using it for machine learning (ML) is prevalent. However, there are increasing concerns about the monopolization and potential breach of private data by greedy and malicious organizations. Interest in Web 3.0 systems is on the rise as an alternative. These systems aim to guarantee the self-sovereignty of personal data in a decentralized setting. Users can share data with others directly for fair compensation. Nevertheless, malicious remote users can still violate the integrity and confidentiality of personal data. Therefore, this paper proposes a novel method of preventing unwanted leakage and counterfeiting of the private data lent on the premise of remote users. This paper focuses on the decentralized nature of Web 3.0 to leverage existing personal storage so that the burden of collecting secure data is relieved. Data owners create a lightweight Docker container to encapsulate their private data sources. The data owners generate another container to be deployed on a remote premise for taking and executing any ML algorithms remote users create. Between the containers forming a distributed trusted execution environment (TEE), data are read through a secure channel. Since the TEE is strictly controlled by the data owner, no malicious ML application can leak or breach the private information. This paper explains the engineering details of how this new method is realized.