Incremental Diversity: An Efficient Anonymization Technique for PPDP of Multiple Sensitive Attributes

Data collected at the organizations such as schools, offices, healthcare centers and e-commerce websites contain multiple sensitive attributes. The information from these organisations marks obtained, salary, disease, treatment traveling history are personal that an individual dislikes to disclose public it may lead privacy threats. Therefore, is necessary preserve of data before publishing. Privacy Preserving Publishing(PPDP) algorithms aim publish without compromising individuals. In recent years several have been designed for PPDP major limitations are, firstly among attributes consider one them primary attribute anonymize data, however there be other dominant need preserved. Secondly, no consistent way categorize Lastly, increased proportion records generated due usage generalization suppression techniques. Hence, overcome current work proposes efficient approach based their semantics using anatomy technique. This reduces residual well categorizes results compared with popular techniques like Simple Distribution Sensitive Values (SDSV) (l, e) diversity. Experiments prove our method outperforms existing methods in terms categorization attributes, reducing percentage preventing