E
Elisa Bertino
Researcher at Purdue University
Publications - 1151
Citations - 38315
Elisa Bertino is an academic researcher from Purdue University. The author has contributed to research in topics: Access control & Information privacy. The author has an hindex of 92, co-authored 1116 publications receiving 35505 citations. Previous affiliations of Elisa Bertino include University of Milan & Peking University.
Papers
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Journal ArticleDOI
State-of-the-art in privacy preserving data mining
Vassilios S. Verykios,Elisa Bertino,Igor Nai Fovino,Loredana Parasiliti Provenza,Yucel Saygin,Yannis Theodoridis +5 more
TL;DR: An overview of the new and rapidly emerging research area of privacy preserving data mining is provided, and a classification hierarchy that sets the basis for analyzing the work which has been performed in this context is proposed.
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TRBAC: A temporal role-based access control model
TL;DR: Temporal-RBAC (TRBAC), an extension of the RBAC model, is introduced, which supports periodic role enabling and disabling, and temporal dependencies among such actions, expressed by means of role triggers.
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A generalized temporal role-based access control model
TL;DR: This work proposes a generalized temporal role-based access control (GTRBAC) model capable of expressing a wider range of temporal constraints and allows expressing periodic as well as duration constraints on roles, user-role assignments, and role-permission assignments.
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The specification and enforcement of authorization constraints in workflow management systems
TL;DR: A language is presented to express both static and dynamic authorization constraints as clauses in a logic program and formal notions of constraint consistency are provided to check the consistency of constraints and assign users and roles to tasks that constitute the workflow in such a way that no constraints are violated.
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Association rule hiding
TL;DR: This work investigates confidentiality issues of a broad category of rules, the association rules, and presents three strategies and five algorithms for hiding a group of associationrules, which is characterized as sensitive.