Security and Privacy when Applying FAIR Principles to Genomic Information

Abstract

Making data Findable, Accessible, Interoperable and Reusable (FAIR) is a good approach when data needs to be shared. However, security and privacy are still critical aspects. In the FAIRification process, there is a need both for de-identification of data and for license attribution. The paper analyses some of the issues related to this process when the objective is sharing genomic information. The main results are the identification of the already existing standards that could be used for this purpose and how to combine them. Nevertheless, the area is quickly evolving and more specific standards could be specified.

FAIR Aspects of a Genomic Information Protection and Management System

“…In order to identify how GIPAMS supports the FAIR principles, we start from the ideas presented in [6], where we analyzed how FAIR principles could be applied to genomic information considering privacy and security. In particular, we discussed in detail steps 3 ("Data de-identification/anonymization") and 6 ("License attribution") of the FAIRification process described in [12].…”

“…Specifically, with respect to license attribution, in [6] we tried to answer to the following four questions: 1) How to express the licenses, 2) How to protect them and guarantee their provenance, 3) How to evaluate their authorization, and 4) How to enforce what they are controlling. The rest of the section provides a first answer to these questions in the context of GIPAMS.…”

“…Another key aspect is to provide the mentioned FAIR principles. We had previously analyzed security and privacy in the context of FAIR [6] and, in this paper, we use some of those results to validate, from a security point of view, that GIPAMS provides FAIR principles.…”

Towards Machine-Readable (Meta) Data and the FAIR Value for Artificial Intelligence Exploration of COVID-19 and Cancer Research Data

“…Innovative solutions in COVID-19 research require more efficient and effective data management strategies and practices. Cancer research is an excellent example of the adoption of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles ( Wilkinson et al, 2016 ) on precision oncology ( Deist et al, 2020 ; Delgado and Llorente, 2020 ; Vesteghem et al, 2020 ) and major cancer data repositories, such as the NIH Cancer Research Data Commons, are gradually adhering to these principles.…”

“…Innovative solutions in COVID-19 research require more efficient and effective data management strategies and practices. Cancer research is an excellent example of the adoption of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles (Wilkinson et al, 2016) on precision oncology (Deist et al, 2020;Delgado and Llorente, 2020;Vesteghem et al, 2020) and major cancer data repositories, such as the NIH Cancer Research Data Commons, are gradually adhering to these principles.In a broad sense, understanding the whole scenario of a worldwide virus outbreak such as the COVID-19 pandemic has been a great challenge. It involves a huge effort to put together facts and a large volume of data related to the dynamics of the real world, as well as past and current results of scientific research.…”