Entry Frank:2013:RMP from tissec.bib

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BibTeX entry

@Article{Frank:2013:RMP,
  author =       "Mario Frank and Joachim M. Buhman and David Basin",
  title =        "Role Mining with Probabilistic Models",
  journal =      j-TISSEC,
  volume =       "15",
  number =       "4",
  pages =        "15:1--15:??",
  month =        apr,
  year =         "2013",
  CODEN =        "ATISBQ",
  DOI =          "https://doi.org/10.1145/2445566.2445567",
  ISSN =         "1094-9224 (print), 1557-7406 (electronic)",
  ISSN-L =       "1094-9224",
  bibdate =      "Thu Apr 4 18:18:20 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tissec.bib",
  abstract =     "Role mining tackles the problem of finding a
                 role-based access control (RBAC) configuration, given
                 an access-control matrix assigning users to access
                 permissions as input. Most role-mining approaches work
                 by constructing a large set of candidate roles and use
                 a greedy selection strategy to iteratively pick a small
                 subset such that the differences between the resulting
                 RBAC configuration and the access control matrix are
                 minimized. In this article, we advocate an alternative
                 approach that recasts role mining as an inference
                 problem rather than a lossy compression problem.
                 Instead of using combinatorial algorithms to minimize
                 the number of roles needed to represent the
                 access-control matrix, we derive probabilistic models
                 to learn the RBAC configuration that most likely
                 underlies the given matrix. Our models are generative
                 in that they reflect the way that permissions are
                 assigned to users in a given RBAC configuration. We
                 additionally model how user-permission assignments that
                 conflict with an RBAC configuration emerge and we
                 investigate the influence of constraints on role
                 hierarchies and on the number of assignments. In
                 experiments with access-control matrices from
                 real-world enterprises, we compare our proposed models
                 with other role-mining methods. Our results show that
                 our probabilistic models infer roles that generalize
                 well to new system users for a wide variety of data,
                 while other models' generalization abilities depend on
                 the dataset given.",
  acknowledgement = ack-nhfb,
  articleno =    "15",
  fjournal =     "ACM Transactions on Information and System Security",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J789",
}

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