MR-I MaxMin-scalable two-phase border based knowledge hiding technique using MapReduce

Abstract

Border based Knowledge hiding techniques (BB-KHT) are widely adopted form of privacy preservation techniques of data mining. These approaches are used to hide sensitive knowledge (confidential information) present in a dataset before sharing or analyzing it. BB-KHT primarily rely on border theory and maximum criterion method for preserving privacy and perpetuating good data quality of sanitized dataset but costs high computational complexity. Further, due to sequential nature, these approaches are particularly felicitous for small datasets and become infeasible while dealing with large scale datasets. Therefore, to subjugate the identified challenges of infeasibility and high computational complexity, a scalable two-phase improved MaxMin BB-KHT using MapReduce framework (MR-I MaxMin) is proposed. The proposed scheme requires only two database scans throughout the hiding process and hence, is computationally inexpensive. Moreover, the scheme also commits to preserve good data quality of sanitized dataset. The MapReduce version of proposed approach helps in achieving the feasibility by processing large voluminous data in a parallel fashion. Quantitative experiments and evaluations have been performed over a number of real and synthetically generated large-scale datasets. It is shown that the proposed MR-I MaxMin technique outperforms the similar existing approaches and vanquishes the identified challenges along with much-needed privacy preservation.