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- W4297884432 abstract "Abstract 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 disclosive and sensitive information. We, therefore, present a possible model to support multidisciplinary research team to protect against disclosure of individual-level data and large datasets used in other disciplines. We argue a 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" @default.
- W4297884432 created "2022-10-01" @default.
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- W4297884432 date "2022-09-20" @default.
- W4297884432 modified "2023-10-17" @default.
- W4297884432 title "SAFEST: A Safeguarding Analytical Framework for dEcentralised Senstive daTa" @default.
- W4297884432 doi "https://doi.org/10.21203/rs.3.rs-2071812/v1" @default.
- W4297884432 hasPublicationYear "2022" @default.
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