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

Abstract

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