Differential Privacy Preserving Algorithm For Data Anonymization

Presently a day the amplifying of data utilization and minimizing protection danger are two clashing objectives. The organization required arrangement of change at the time of discharge information. While deciding the best arrangement of changes has been the concentrate on the broad work in the database group, the scalability and security are significant issues while information change. The privacy preserving method called K-anonymity is acquainted with conquer this issue. In this procedure, every one of the data records are divided into some number of datasets and each record in a specific dataset must be vague with different records in that dataset. But, this technique is vulnerable to some attacks. Thus another method called l-diversity qualities is acquainted with keep away from foundation assaults on the anonymized information.