Journal international des progrès technologiques
Libre accès

Abstrait

SAFEST: A Safeguarding Analytical Framework for Decentralised Sensitive Data

Patricia Ryser-Welch1*, Leire Abarrantegui2, Soumya Banerjee3

An increasing demand and dependence of analyzing a data has been driven by “big data” and “Internet of Things (IoT)”. Scientific reproducibility, robustness and the cost of capturing new data has been improved through findable, accessible, interoperable, and reusable data sharing. Ethical and legal restrictions impose the use of privacy preservation and protection measures for any disclosure and sensitive information. We, therefore, present a possible model to support multi-disciplinary research team to protect against disclosure of individual-level data and large datasets used in other disciplines. We argue technology reliance is not enough and a continuous collaboration that adapt to new cyber-security, and data inferential threat is needed. We consequently conclude some standards could lead to closer collaboration to support research and innovation in the long term.