K-Anonymization Approach FOR Privacy Preserving IN Data Mining

Data is collected and processed using diverse sources and tools that lead to privacy issues. Till now randomization, k-anonymization model, l diversity, t closeness, cryptography and many more techniques have been used to preserve the privacy of an individual. But each and every technique have their own demerits i.e. Information Loss, Privacy breached, Low Data Utility. Among all these approach, k anonymization approach one of the mostly used anonymization based approach. However, this approach suffers from the issue of information loss. So, it is challenging task for data miner to mine data. The paper focus to decrease information loss using the 2 level k anonymization approach and also preserve privacy as compared to the traditional approach. The main aim of this approach is to decrease data loss and no compromise with privacy.

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