Top-Down Heuristics for Multi-Dimensional Partitioning To Satisfy Imprecision Bounds

Abstract

Role-based Access Control (RBAC) agrees to important permissions on objects based on roles in an organization. An RBAC policy configuration is collected of a set of Users (U), a set of Roles (R), and a set of Permissions (P). The access control policies describe selection predicates obtainable to roles at the same time as the privacy obligation is to please the k-anonymity orl-diversity. An additional constraint that wants to be content by the PPM is the imprecision bound for each selection predicate. In our formulation of the aforementioned problem we suggest heuristics for anonymization algorithms and demonstrate empirically that the proposed approach gratify imprecision bounds for additional permissions and has subordinate total imprecision than the present state of the art.

Authors and Affiliations

V Anasuya, Penmetsa V. Krishna Raja, Aruna Rekha Gollapalli

Keywords

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  • EP ID EP28274
  • DOI -
  • Views 313
  • Downloads 3

How To Cite

V Anasuya, Penmetsa V. Krishna Raja, Aruna Rekha Gollapalli (2015). Top-Down Heuristics for Multi-Dimensional Partitioning To Satisfy Imprecision Bounds. International Journal of Research in Computer and Communication Technology, 4(10), -. https://europub.co.uk/articles/-A-28274