Parallelizing K-Anonymity Algorithm for Privacy Preserving Knowledge Discovery from Big Data

摘要

Disclosure control has become inevitable as privacy is given paramount importance while publishing data for mining. The data mining community enjoyed revival after Samarti and Sweeney proposed k-anonymization for privacy preserving data mining. The k-anonymity has gained high popularity in research circles. Though it has some drawbacks and other PPDM algorithms such as 1-diversity, t-closeness and m-privacy came into existence, the anonymization techniques are widely used for preserving privacy. With the emergence of big data and big data analytics, it is the time to redefine PPDM algorithms to be compatible with MapReduce programming paradigm in cloud computing environment. The paradigm shift is required for two reasons. First, it is required to face the challenges of big data and its processing. Second, it is required as MapReduce can leverage the parallel processing power of Graphics Processing Unit (GPU) and the cloud infrastructure. In this paper we proposed an algorithm to parallelize k-anonymity. We made an empirical study and evaluated the algorithm using MapReduce programming with Hadoop as distributed programming framework. The results revealed that the proposed algorithm works fine with the new programming model.