Research Article Open Access

A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using Map Reduce On Cloud

Abstract

Releasing person-specific data in its most specific state poses a threat to individual privacy. This paper presents a practical and productive algorithm for determining a abstract version of data that masks sensitive information and remains useful for standardizing organization. The classification of data is implemented by specializing or detailing the level of information in a top-down manner until a minimum privacy requirement is compromised. This top-down specialization is practical and efficient for handling both definitive and continuous attributes. Our method exploits the scenario that data usually contains redundant structures for classification. While generalization may remove few structures, other structures emerge to help. Our results show that standard of classification can be preserved even for highly prohibitive privacy requirements. This work has great applications to both public and private sectors that share information for mutual advantage and productivity.

Shweta Sunil Bhand, Prof.J.L.Chaudhari

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