PRIVACY PRESERVED ACCESS CONTROL FOR RDBMS
Privacy preserved data mining methods are used to protect the sensitive attributes in knowledge discovery process. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy preservation process. Clustering method is used to group up the records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship. Census data and medical data are referred as micro data. User permissions are managed with dynamic data and policy management mechanism with privacy. Privacy Protection Mechanism (PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracy-constrained privacy-preserving access control framework is used to manage access control in relational database. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the k-anonymity or l- diversity. Imprecision bound constraint is assigned for each selection predicate. Role-based Access Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection Mondrian (TDSM) algorithm is used for query workload-based anonymization. Query cuts are selected with minimum bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in Top-Down Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries. The policy based access control mechanism is enhanced to support dynamic data management technique. Data insert, delete and update operations are connected with the partition management mechanism. Cell level access control is provided with differential privacy method. Dynamic role management model is integrated with the access control policy mechanism for query predicates.