Enhancing data publishing privacy: split-and-mould, an algorithm for equivalent specification

Abstract

Data sharing and publication have been popular in recent years due to the abundance of options. Evaluating and extracting data from sizable valuable databases i.e., data mining has various challenges which include issues with security, privacy, and data integrity. Anonymized data is used in the majority of privacy preserving data publication approaches, depending on a few utilitarian measures. However, applications that have particular needs for the data they utilize might not be able to use the anonymized data. Practical data anonymization must work to accomplish two opposing objectives: to maintain the data’s usefulness and to satisfy a specific privacy need.
The utility loss when data is anonymized is frequently measured using generic utility metrics, such as the specific values generalized in a specific ontology. As a need for an application, we suggest equivalent specification, a technique that enables a data user to characterize some properties of the anonymized data. We also introduce the “split-and-mould” algorithm, a heuristic anonymization algorithm that applies a generalization method to the user-provided parameters. Our preliminary results indicate that the specification format and procedure can improve significantly the utility of the anonymized data for data mining that develop predictive models, like decision trees (DTs) and Naïve Bayes.