Two-Phase Top-down Specialization for High Scalability and Privacy Concerns

Provided by: International Journal of Advanced Research in Computer Science & Technology (IJARCST)
Topic: Big Data
Format: PDF
Sharing the private data like financial transaction record in its most specific state poses a threat to individual privacy. MapReduce algorithm for determining generalization and provide protection for sensitive information. Data sets are generalized in a top-down manner until k-anonymity is violated, in order to expose the maximum utility. This top-down specialization is efficient for high scalability and privacy concerns. High scalable two-phase top-down approach to anonymize large-scale data using mapreduce is proposed. Experimental evaluation result shows that security and privacy preservation of top-down specialization can be significantly improved over existing approach.

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