An intensified approach for privacy preservation in incremental data mining

Abstract

Mankind has achieved an impressive ability to store data. The capacity of digital data storage has doubled every nine months for at least a decade. Furthermore, our skills and interest to collect data and analyze them are also remarkable. There has been a wide variety of research going on in the field of privacy preservation in data mining. Most of the methods are implemented for static data. But the world is filled with dynamic data which grows rapidly than what we expect. No technique is better than the other ones with respect to all criteria. This paper focus on privacy criteria that provide formal safety guar-antees, present algorithms that sanitize data to make it safe for release while preserving useful information, and discuss ways of analyzing the sanitized data. This paper focus on a methodology that is well suited for incremental data that preserves its privacy while also performing an efficient mining.The method does not require the entire data to be processed again for the insertion of new data. The method uses frequency discretization technique that represents the in-terestingness of items in a database as a pattern. This method is suggested for both incremental data and providing privacy for such data. We develop the algorithm for making the database flexible in terms of mining and cost effective in terms of storage.