We study the problem of inferring policy intent to identify
\nmiscon gurations in access control implementations. This is in contrast
\nto traditional role-mining techniques, which focus on creating better ab-
\nstractions for access control management. We show how raw metadata
\ncan be summarized e ectively, by grouping together users with similar
\npermissions over shared resources. Using these summary statements, we
\napply statistical techniques to detect outliers, which we classify as se-
\ncurity and accessibility miscon gurations. Speci cally, we show how our
\ntechniques for mining policy intent are robust, and have strong consis-
\ntency and convergence guarantees.<\/p>\n","protected":false},"excerpt":{"rendered":"
We study the problem of inferring policy intent to identify miscon gurations in access control implementations. This is in contrast to traditional role-mining techniques, which focus on creating better ab- stractions for access control management. We show how raw metadata can be summarized e ectively, by grouping together users with similar permissions over shared resources. 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