Attribute-based access control (ABAC) provides a high level of flexibility that promotes security and information sharing. ABAC policy mining algorithms have potential to significantly reduce the cost of migration to ABAC, by partially automating the development of an ABAC policy from an access control list (ACL) policy or role-based access control (RBAC) policy with accompanying attribute data. This paper presents an ABAC policy mining algorithm. To the best of our knowledge, it is the first ABAC policy mining algorithm. Our algorithm iterates over tuples in the given user-permission relation, uses selected tuples as seeds for constructing candidate rules, and attempts to generalize each candidate rule to cover additional tuples in the user-permission relation by replacing conjuncts in attribute expressions with constraints. Our algorithm attempts to improve the policy by merging and simplifying candidate rules, and then it selects the highest-quality candidate rules for inclusion in the generated policy.

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Abstract. Attribute-based access control (ABAC) provides a high level of flexibility that promotes security and information sharing. ABAC policy mining algorithms have potential to significantly reduce the cost of migration to ABAC, by partially automating the development of an ABAC policy from information about the existing access-control policy and attribute data. This paper presents an algorithm for mining ABAC policies from operation logs and attribute data. To the best of our knowledge, it is the first algorithm for this problem.

In large organizations, access control policies are managed by multiple users (administrators). An administrative policy specifies how each user in an enterprise may change the policy. Fully understanding the consequences of an administrative policy in an enterprise system can be difficult, because of the scale and complexity of the access control policy and the administrative policy, and because sequences of changes by different users may interact in unexpected ways. Administrative policy analysis helps by answering questions such as user-permission reachability, which asks whether specified users can together change the policy in a way that achieves a specified goal, namely, granting a specified permission to a specified user. This paper presents a rule-based access control policy language, a rule-based administrative policy model that controls addition and removal of facts and rules, and an abductive analysis algorithm for user-permission reachability. Abductive analysis means that the algorithm can analyze policy rules even if the facts initially in the policy (e.g., information about users) are unavailable. The algorithm does this by computing minimal sets of facts that, if present in the initial policy, imply reachability of the goal.