MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data

摘要

This paper presents a novel ‘MapReduce Based Optimum Victim Item and Transaction Selection Approach (MR-OVnTSA)’ that provides a feasible and intelligent solution for protecting sensitive frequent itemsets present in big data. The approach advocates to resolve the captious challenges, existing knowledge hiding algorithms are encountering. The proposed solution optimally minimizes the side effect of hiding process on non-sensitive information, and maintains a balance between knowledge and privacy as well as handles the exponential growth in data volume efficiently. The algorithm plugs the most optimum item and transaction as victim, by intelligently analyzing their coverage value i.e. it chooses one with maximal impact on sensitive knowledge but minimal on non-sensitive information. Further, the MapReduce version of the proposed scheme resolves the issue of non-feasibility by processing large-scale data (big data) in a parallel fashion. Experiments have been demonstrated over real and synthetically generated large-scale datasets. Results evince that the proffered scheme is much more efficient and maintains the balance between the privacy preservation, data quality maintenance, and CPU time, when dealing with large voluminous big datasets compared to existing knowledge hiding techniques.