# Certifying Provenance of Scientific Datasets with Self-sovereign Identity and Verifiable Credentials

arXiv: https://arxiv.org/abs/2004.02796 · HTML: https://mlsanity.com/paper/2004.02796/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions](https://mlsanity.com/paper/2103.03610/) (2021)
- [Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials](https://mlsanity.com/paper/2105.06370/) (2021)

## Abstract (arXiv)

In order to increase the value of scientific datasets and improve research outcomes, it is important that only trustworthy data is used. This paper presents mechanisms by which scientists and the organisations they represent can certify the authenticity of characteristics and provenance of any datasets they publish so that secondary users can inspect and gain confidence in the qualities of data they source. By drawing on data models and protocols used to provide self-sovereign ownership of identity and personal data to individuals, we conclude that providing self-sovereignty to data assets offers a viable approach for institutions to certify qualities of their datasets in a cryptography secure manner, and enables secondary data users to efficiently perform verification of the authenticity of such certifications. By building upon emerging standards for decentralized identification and cryptographically verifiable credentials, we envisage an infrastructure of tools being developed to foster adoption of metadata certification schemes, and improving the quality of information provided in support of shared data assets.