Privacy Preserving Sequential Pattern Mining in Progressive Databases using Noisy Data

Research in the area of privacy preserving techniques in databases and subsequently in data mining concepts have witnessed an explosive growth-spurt in recent years. This work investigates the problem of privacy-preserving mining of frequent sequential patterns over progressive databases. We propose a procedure to protect the privacy of data by adding noisy items to each transaction. The experimental results indicate that this method can achieve a rather high level of accuracy. The method is applied on an existing algorithm PISA for frequent pattern mining. This algorithm works on both static and dynamically increasing databases, and thereby takes full advantage of their applicability of the module.