In microdata releases, main task is to protect the privacy of data subjects. Microaggregation technique use to disclose the limitation at protecting the privacy of microdata. This technique is an alternative to generalization and suppression, which use to generate k-anonymous data sets. In this dataset, identity of each subject is hidden within a group of k subjects. Microaggregation perturbs the data and additional masking allows refining data utility in many ways, like increasing data granularity, to avoid discretization of numerical data, to reduce the impact of outliers. If the variability of the private data values in a group of k subjects is too small, k-anonymity does not provide protection against attribute disclosure. In this work Role based access control is assumed. The access control policies define selection predicates to roles. Then use the concept of imprecision bound for each permission to define a threshold on the amount of imprecision that can be tolerated. So the proposed approach reduces the imprecision for each selection predicate. Anonymization is carried out only for the static relational table in the existing papers. Privacy preserving access control mechanism is applied to the incremental data
