Blockchain-backed analytics. Adding blockchain-based quality gates to data science projects.

Abstract

A typical analytical lifecycle in data science projects starts with the process of data generation and collection, continues with data preparation and preprocessing and heads towards project specific analytics, visualizations and presentations. In order to ensure high quality trusted analytics, every relevant step of the data-model-result linkage needs to meet certain quality standards that furthermore should be certified by trusted quality gate mechanisms.We propose “blockchain-backed analytics”, a scalable and easy-to-use generic approach to introduce quality gates to data science projects, backed by the immutable records of a blockchain. For that reason, data, models and results are stored as cryptographically hashed fingerprints with mutually linked transactions in a public blockchain database.This approach enables stakeholders of data science projects to track and trace the linkage of data, applied models and modeling results without the need of trust validation of escrow systems or any other third party.

Enhancing IoT Data Dependability through a Blockchain Mirror Model

“…The issue has been addressed by implementing persistent identifiers (e.g., CoolURI, Digital Object Identifier, National Bibliography Number) and digital preservation services [9,10] in order to guarantee the trustworthiness of the digital resources exploited. The introduction of blockchain technology intuitively opens new technical possibilities to build trust for digital datasets that have been only partially explored in the current publications [11,12]. In particular, a formal argumentation of the precise meaning of adding trust to a dataset has not been provided yet.…”