Identity Lock – Privacy-Preserving Data Publishing Tool

Abstract

In today’s world, data sharing is very common. Currently, the strong movement is occurring towards publishing data for statistical studies. In this case, data publishers are providing some sort of data to the research field, but they do not know what kind of things the 3rd party can do with the data provided to them. Data preservation is an important aspect when sharing data because attackers can easily disclose a person's identity and their personal information. Hence, in order to secure privacy, different methodologies are implemented on data. This paper presents Identity Lock - the Privacy-Preserving Data Publishing (PPDP) tool, uses various anonymization techniques and implements k-Incognito, l-Incognito and ?-Differential Privacy algorithm to hide and anonymize data. The software also performs the experimental evaluation in order to calculate the performance of the algorithm on the basis of how much utility and privacy is maintained.