EPJ Web Conf.
Volume 214, 201923rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|Number of page(s)||7|
|Section||T3 - Distributed computing|
|Published online||17 September 2019|
Federated Identity Management for Research
University of Chicago and Internet2,
2 DAASI International, Germany,
3 STFC UK Research and Innovation, Rutherford Appleton Laboratory, Didcot, United Kingdom
5 European Organisation for Nuclear Research (CERN), Geneva, Switzerland
6 Karlsruhe Institute of Technology, Germany
* e-mail: email@example.com
Published online: 17 September 2019
Federated identity management (FIM) is an arrangement that can be made among multiple organisations that lets subscribers use the same identification data to obtain access to the secured resources of all organisations in the group. In many research communities there is an increasing interest in a common approach to FIM as there is obviously a large potential for synergies. FIM4R  provides a forum for communities to share challenges and ideas, and to shape the future of FIM for our researchers. Current participation covers high energy physics, life sciences and humanities, to mention but a few. In 2012 FIM4R converged on a common vision for FIM, enumerated a set of requirements and proposed a number of recommendationsfor ensuring a roadmap for the uptake of FIM . In summer 2018, FIM4R published an updated version of this paper . The High Energy Physics (HEP) Community has been heavily involved in creating both the original white paper and the new version, which documented the progress made in FIM for Research, in addition to the current challenges. This paper presents the conclusions of this second FIM4R white paper and a summary of the identified requirements and recommendations. We focus particularly on the direction being taken by the Worldwide LHC Computing Grid (WLCG), through the WLCG Authorisation Working Group, and the requirements gathered from the HEP Community.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Initial download of the metrics may take a while.