Applying Tuple Migration to Preserve Privacy in Databases

Nowadays, in the age of big data, cloud computing, and the internet of things, there has been the ubiquity of open data for people to conveniently approach, use, and mine. However, most of the valuable data is data relating to personal sensitive information such as data about diseases or salaries. This data should be protected by some policies to conceal the relationship between sensitive data and people. As of late, there are many approaches and techniques for preserving data privacy, of which k-anonymity is the most popular. Nevertheless, most of the k-anonymity algorithms are too general and do not concentrate on any concrete data mining technique, so the data utility does not remain high. In this work, we introduce a k-anonymity algorithm based on tuple member migration between tuple groups to achieve k-anonymity while preserving data quality for a data mining algorithm, i.e., association rule mining because it is one of the most popular data mining techniques which discovers the association of items or itemsets in a dataset. The algorithm was evaluated on the adult dataset to assess the performance as well as the data utility.