Scalable l-Diversity: An Extension to Scalable k-Anonymity for Privacy Preserving Big Data Publishing

Introduction

The success and failure of any organizations highly depend on the analysis of their business/transaction data. But size of such data is in massive form; hence, it cannot be analyzed by traditional analytical methods. To analyze and handle these data, distributed environment such as MapReduce framework (Dean & Ghemawat, 2008) is required, in which this large volume of data can be distributed over many distributed systems to process and analyze it. Almost every organization used to make their business data public for the use of researchers. This business/transaction data contains private information of their customers, so organizations need to anonymize their data before publishing it publicly.

Preserving privacy as well as to keep the high utility of data is a big challenge in order to publish the big data because data are collected from different sources which may leads to privacy issues (Wu, Zhu, Wu & Ding, 2014; Mehta & Rao, 2016). The anonymization of data will reduce the utility of underlying data. Preserving the privacy of an individual in order to publish the big data with high utility is a challenging task and can be considered as open research problem. In this paper, the discussion about two privacy model k-anonymity and l-diversity is given and further we propose scalable algorithms for k-anonymity and l-diversity. The results of SKA and SLD for different value of k and l for large dataset are also compared.