An Effective privacy preserving and Accuracy Constrained Method for Relational Data

Abstract: Now-a-days, the usage of www has increased and huge amount of data is getting generated and managed in every second, 20-30% of total volume of data contains sensitive information which needs to be managed with data security mechanism. The data should be exposed through proper access control mechanism. To manage sensitive data more over many Privacy Protection mechanisms are implemented which might not claim complete durability every time. In this situation, to pass proper authentication user needs to share different identity disclosure parameters which also are maintained as generalized data. Privacy protection method use suppression and generalization of relational data to anonymized against attribute disclosure. Normally below processes are used to manage these requirements k-anonymity and l-diversity against identity and attribute disclosure. Anyway the data security and privacy is managed based on only the authorized information. The access control paradigm follows k-anonymity and l-diversity satisfying the data privacy. Imprecision bound validation by PPM should pass to manage privacy control. Technique of workload-aware anonymization is one concern for this assignment, but as multiple role based privacy protection does not bring any significance with this approach.

Keywords: Access control, privacy, k-anonymity, privacy preservation.