Precision Controlled Secrecy Stabilization in Relational Data
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 1
Abstract
Abstract: Data privacy issues are increasingly becoming important for many applications. Protective individual privacy is a crucial downside. However, sensitive data will still be ill-used by approved users to compromise the privacy of shoppers. Traditionally, research in the database community in the area of data security can be broadly classified into access control research and data privacy research. Access Control Mechanisms (ACM) is used to ensure that only authorized information is available to users. Privacy Protection Mechanism (PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. The privacy preservation can be achieved through anonymization techniques like generalization or suppression. Along with privacy the precision of the authorized data is important. The aim of the work is to provide better security and minimum level of precision to the retrieved data, for that in this paper an accuracyconstrained privacy preserving access control mechanism is implemented with additional constraint on each selection predicate called imprecision bounds. The accuracy constraints are satisfied for multiple roles also. Along with that a workload aware anonymization concept is used for selection predicates. The experimental results shows proposed system with multilevel anonymization works better in terms of precision, privacy for more permissions than current state of art.
Authors and Affiliations
Rajkumar Lingamgunta , Jajula Hari Babu
Keywords
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