A Comparative Study on Privacy Preserving Association Rule Mining Algorithms

The field of privacy has seen rapid advances in recent years because of the increases in the ability to store data. In particular, recent advances in the data mining field have lead to increased concerns about privacy. Privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. As a result privacy becomes one of the prime concerns in data mining research community. A new class of data mining methods, known as privacy preserving data mining algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of digital data, while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one to hide sensitive item sets or patterns, before the data mining process is executed. Since association rule mining is one of the important problems in data mining, privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. In this work, we present an evaluation study for estimating and comparing different kinds of privacy preserving association rule mining algorithms.