NIST Big Data Interoperability Framework: volume 4, security and privacy, version 2

Abstract

NIST Special Publication series 1500 is intended to capture external perspectives related to NIST standards, measurement, and testing-related efforts. These external perspectives can come from industry, academia, government, and others. These reports are intended to document external perspectives and do not represent official NIST positions. Certain commercial entities, equipment, or materials may be identified in this document to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. There may be references in this publication to other publications currently under development by NIST in accordance with its assigned statutory responsibilities. The information in this publication, including concepts and methodologies, may be used by federal agencies even before the completion of such companion publications. Thus, until each publication is completed, current requirements, guidelines, and procedures, where they exist, remain operative. For planning and transition purposes, federal agencies may wish to closely follow the development of these new publications by NIST. Organizations are encouraged to review all draft publications during public comment periods and provide feedback to NIST. All NIST publications are available at http://www.nist.gov/publication-portal.cfm.

Towards Data Privacy and Security Framework in Big Data Governance

“…According to Gartner, the technology used in those stages are not suitable and does not cope up with current technology development (Gartner, 2019). Furthermore, since 2015, big data was not considered in the yearly hype cycle report as big data has entered a higher phase from its previous (Nancy Davis Kho, 2018). Big data is also associated with the need for coordination in data handling especially in data-intensive applications.…”